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Fujii I, Matsumoto N, Ogawa M, Konishi A, Kaneko M, Watanabe Y, Masuzaki R, Kogure H, Koizumi N, Sugitani M. Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study. Diagnostics (Basel) 2024; 14:2585. [PMID: 39594250 PMCID: PMC11593288 DOI: 10.3390/diagnostics14222585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2024] [Revised: 10/31/2024] [Accepted: 11/07/2024] [Indexed: 11/28/2024] Open
Abstract
BACKGROUND/OBJECTIVES Elastography increased the diagnostic accuracy of liver fibrosis. However, several challenges persist, including the widespread utilization of equipment, difficulties in measuring certain cases, and the influence of viscosity factors. A rough surface and a blunted hepatic margin have long been acknowledged as valuable characteristics indicative of hepatic fibrosis. The objective of this study was to conduct an image analysis and quantitative assessment of the contour of the sagittal section of the left lobe of the liver. METHODS Between February and October 2020, 486 consecutive outpatients underwent ultrasound examinations at our hospital. A total of 214 images were manually annotated by delineating the liver contour to create annotation images. U-Net was employed for liver segmentation, with the dataset divided into training (n = 128), testing (n = 42), and validation (n = 44) subsets. Additionally, 43 Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) cases with pathology data from between 2015 and 2020 were included. Segmentation was performed using the program developed in the first step. Subsequently, shape analysis was conducted using ImageJ. RESULTS Liver segmentation exhibited high accuracy, as indicated by Dice loss of 0.044, Intersection over Union of 0.935, and an F score of 0.966. The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. Image analysis showed MinFeret and Minor correlated with liver fibrosis stage (p = 0.046, 0.036, respectively). Sensitivity, specificity, and AUROC of Minor for ≥F3 were 0.571, 0.862, and 0.722, respectively, and F4 were 1, 0.600, and 0.825, respectively. CONCLUSION Deep learning segmentation of the sagittal cross-sectional contour of the left lobe of the liver demonstrated commendable accuracy. The roughness of the liver surface was correctly judged by artificial intelligence. Image analysis showed the thickness of the left lobe inversely correlated with liver fibrosis stage.
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Affiliation(s)
- Itsuki Fujii
- Department of Mechanical Engineering and Intelligent Systems, Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu 182-8585, Japan
| | - Naoki Matsumoto
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Masahiro Ogawa
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Aya Konishi
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Masahiro Kaneko
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Yukinobu Watanabe
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Ryota Masuzaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Hirofumi Kogure
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan; (M.O.); (Y.W.)
| | - Norihiro Koizumi
- Department of Mechanical Engineering and Intelligent Systems, Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu 182-8585, Japan
| | - Masahiko Sugitani
- Division of Pathology, Nihon University School of Medicine, Tokyo 173-8610, Japan;
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Ramamoorthy K, Rajaguru H. Exploitation of Bio-Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images. Biomimetics (Basel) 2024; 9:356. [PMID: 38921235 PMCID: PMC11201414 DOI: 10.3390/biomimetics9060356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 06/27/2024] Open
Abstract
In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through the Gaussian mixture model (GMM), Softmax discriminant classifier (SDC), harmonic search algorithm (HSA), SVM (linear), SVM (RBF), SVM (polynomial), artificial algae optimization (AAO), and hybrid classifier artificial algae optimization (AAO) with Gaussian mixture mode (GMM). The classifiers' performances are compared based on accuracy, F1 Score, MCC, F measure, error rate, and Jaccard metric (JM). The hybrid classifier AAO-GMM, with the PFCM feature, outperforms the other classifiers and attained an accuracy of 99.03% with an MCC of 0.90.
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Affiliation(s)
| | - Harikumar Rajaguru
- Department of ECE, Bannari Amman Institute of Technology, Tamil Nadu 638401, India
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Lin Y, Wang J, Liu Q, Zhang K, Liu M, Wang Y. CFANet: Context fusing attentional network for preoperative CT image segmentation in robotic surgery. Comput Biol Med 2024; 171:108115. [PMID: 38402837 DOI: 10.1016/j.compbiomed.2024.108115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/27/2024]
Abstract
Accurate segmentation of CT images is crucial for clinical diagnosis and preoperative evaluation of robotic surgery, but challenges arise from fuzzy boundaries and small-sized targets. In response, a novel 2D segmentation network named Context Fusing Attentional Network (CFANet) is proposed. CFANet incorporates three key modules to address these challenges, namely pyramid fusing module (PFM), parallel dilated convolution module (PDCM) and scale attention module (SAM). Integration of these modules into the encoder-decoder structure enables effective utilization of multi-level and multi-scale features. Compared with advanced segmentation method, the Dice score improved by 2.14% on the dataset of liver tumor. This improvement is expected to have a positive impact on the preoperative evaluation of robotic surgery and to support clinical diagnosis, especially in early tumor detection.
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Affiliation(s)
- Yao Lin
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Jiazheng Wang
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China.
| | - Qinghao Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Kang Zhang
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Min Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China; Research Institute of Hunan University in Chongqing, Chongqing, 401135, China.
| | - Yaonan Wang
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
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Hardy R, Klepich J, Mitchell R, Hall S, Villareal J, Ilin C. Improving nonalcoholic fatty liver disease classification performance with latent diffusion models. Sci Rep 2023; 13:21619. [PMID: 38062049 PMCID: PMC10703886 DOI: 10.1038/s41598-023-48062-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance even in low-data regime settings. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fréchet Inception Distance (FID), computed on diffusion- and generative adversarial network (GAN)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of 1.90 compared to 1.67 for GANs, and a minimum FID score of 69.45 compared to 100.05 for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of 0.904 on a NAFLD prediction task.
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Affiliation(s)
- Romain Hardy
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | - Joe Klepich
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | - Ryan Mitchell
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | - Steve Hall
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | | | - Cornelia Ilin
- School of Information, U.C. Berkeley, Berkeley, CA, USA.
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Nduma BN, Al-Ajlouni YA, Njei B. The Application of Artificial Intelligence (AI)-Based Ultrasound for the Diagnosis of Fatty Liver Disease: A Systematic Review. Cureus 2023; 15:e50601. [PMID: 38222117 PMCID: PMC10788148 DOI: 10.7759/cureus.50601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2023] [Indexed: 01/16/2024] Open
Abstract
Fatty liver disease, also known as hepatic steatosis, poses a significant global health concern due to the excessive accumulation of fat within the liver. If left untreated, this condition can give rise to severe complications. Recent advances in artificial intelligence (AI, specifically AI-based ultrasound imaging) offer promising tools for diagnosing this condition. This review endeavors to explore the current state of research concerning AI's role in diagnosing fatty liver disease, with a particular emphasis on imaging methods. To this end, a comprehensive search was conducted across electronic databases, including Google Scholar and Embase, to identify relevant studies published between January 2010 and May 2023, with keywords such as "fatty liver disease" and "artificial intelligence (AI)." The article selection process adhered to the PRISMA framework, ultimately resulting in the inclusion of 13 studies. These studies leveraged AI-assisted ultrasound due to its accessibility and cost-effectiveness, and they hailed from diverse countries, including India, China, Singapore, the United States, Egypt, Iran, Poland, Malaysia, and Korea. These studies employed a variety of AI classifiers, such as support vector machines, convolutional neural networks, multilayer perceptron, fuzzy Sugeno, and probabilistic neural networks, all of which demonstrated a remarkable level of precision. Notably, one study even achieved a diagnostic accuracy rate of 100%, underscoring AI's potential in diagnosing fatty liver disease. Nevertheless, the review acknowledged certain limitations within the included studies, with the majority featuring relatively small sample sizes, often encompassing fewer than 100 patients. Additionally, the variability in AI algorithms and imaging techniques added complexity to the comparative analysis. In conclusion, this review emphasizes the potential of AI in enhancing the diagnosis and management of fatty liver disease through advanced imaging techniques. Future research endeavors should prioritize the execution of large-scale studies that employ standardized AI algorithms and imaging techniques to validate AI's utility in diagnosing this prevalent health condition.
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Affiliation(s)
- Basil N Nduma
- Internal Medicine, Merit Health Wesley, Hattiesburg, USA
| | | | - Basile Njei
- Medicine, Yale School of Medicine, New Haven, USA
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Singh S, Hoque S, Zekry A, Sowmya A. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review. J Med Syst 2023; 47:73. [PMID: 37432493 PMCID: PMC10335966 DOI: 10.1007/s10916-023-01968-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 07/02/2023] [Indexed: 07/12/2023]
Abstract
Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.
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Affiliation(s)
- Sonit Singh
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia.
| | - Shakira Hoque
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Amany Zekry
- St George and Sutherland Clinical Campus, School of Clinical Medicine, UNSW, High St, Kensington, 2052, NSW, Australia
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Arcot Sowmya
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia
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Yao Y, Zhang Z, Peng B, Tang J. Bio-Inspired Network for Diagnosing Liver Steatosis in Ultrasound Images. Bioengineering (Basel) 2023; 10:768. [PMID: 37508795 PMCID: PMC10376777 DOI: 10.3390/bioengineering10070768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
Using ultrasound imaging to diagnose liver steatosis is of great significance for preventing diseases such as cirrhosis and liver cancer. Accurate diagnosis under conditions of low quality, noise and poor resolutions is still a challenging task. Physiological studies have shown that the visual cortex of the biological visual system has selective attention neural mechanisms and feedback regulation of high features to low features. When processing visual information, these cortical regions selectively focus on more sensitive information and ignore unimportant details, which can effectively extract important features from visual information. Inspired by this, we propose a new diagnostic network for hepatic steatosis. In order to simulate the selection mechanism and feedback regulation of the visual cortex in the ventral pathway, it consists of a receptive field feature extraction module, parallel attention module and feedback connection. The receptive field feature extraction module corresponds to the inhibition of the non-classical receptive field of V1 neurons on the classical receptive field. It processes the input image to suppress the unimportant background texture. Two types of attention are adopted in the parallel attention module to process the same visual information and extract different important features for fusion, which improves the overall performance of the model. In addition, we construct a new dataset of fatty liver ultrasound images and validate the proposed model on this dataset. The experimental results show that the network has good performance in terms of sensitivity, specificity and accuracy for the diagnosis of fatty liver disease.
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Affiliation(s)
- Yuan Yao
- General Practice Medical Center, West China Hospital, Sichuan University, Chengdu 610044, China
| | - Zhenguang Zhang
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China
| | - Bo Peng
- School of Computing and Artificial Intelligent, Southwest Jiaotong University, Chengdu 611756, China
| | - Jin Tang
- Tiaodenghe Community Health Service Center, Chengdu 610066, China
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8
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Weng S, Hu D, Chen J, Yang Y, Peng D. Prediction of Fatty Liver Disease in a Chinese Population Using Machine-Learning Algorithms. Diagnostics (Basel) 2023; 13:diagnostics13061168. [PMID: 36980476 PMCID: PMC10047083 DOI: 10.3390/diagnostics13061168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/13/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Fatty liver disease (FLD) is an important risk factor for liver cancer and cardiovascular disease and can lead to significant social and economic burden. However, there is currently no nationwide epidemiological survey for FLD in China, making early FLD screening crucial for the Chinese population. Unfortunately, liver biopsy and abdominal ultrasound, the preferred methods for FLD diagnosis, are not practical for primary medical institutions. Therefore, the aim of this study was to develop machine learning (ML) models for screening individuals at high risk of FLD, and to provide a new perspective on early FLD diagnosis. METHODS This study included a total of 30,574 individuals between the ages of 18 and 70 who completed abdominal ultrasound and the related clinical examinations. Among them, 3474 individuals were diagnosed with FLD by abdominal ultrasound. We used 11 indicators to build eight classification models to predict FLD. The model prediction ability was evaluated by the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and kappa value. Feature importance analysis was assessed by Shapley value or root mean square error loss after permutations. RESULTS Among the eight ML models, the prediction accuracy of the extreme gradient boosting (XGBoost) model was highest at 89.77%. By feature importance analysis, we found that the body mass index, triglyceride, and alanine aminotransferase play important roles in FLD prediction. CONCLUSION XGBoost improves the efficiency and cost of large-scale FLD screening.
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Affiliation(s)
- Shuwei Weng
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| | - Die Hu
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| | - Jin Chen
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| | - Yanyi Yang
- Health Management Center, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Daoquan Peng
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Alshagathrh FM, Househ MS. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120748. [PMID: 36550954 PMCID: PMC9774180 DOI: 10.3390/bioengineering9120748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/30/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. OBJECTIVE This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. METHODOLOGY A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. RESULTS Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). CONCLUSION AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.
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Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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12
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Liu JQ, Ren JY, Xu XL, Xiong LY, Peng YX, Pan XF, Dietrich CF, Cui XW. Ultrasound-based artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2022; 28:5530-5546. [PMID: 36304086 PMCID: PMC9594013 DOI: 10.3748/wjg.v28.i38.5530] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/12/2022] [Accepted: 09/22/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.
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Affiliation(s)
- Ji-Qiao Liu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jia-Yu Ren
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xiao-Lan Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Li-Yan Xiong
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian 116000, Liaoning Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3003, Switzerland
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
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Senarathna J, Kovler M, Prasad A, Bhargava A, Thakor N, Sodhi CP, Hackam DJ, Pathak AP. In vivo phenotyping of the microvasculature in necrotizing enterocolitis with multicontrast optical imaging. Microcirculation 2022; 29:e12768. [PMID: 35593520 PMCID: PMC9633336 DOI: 10.1111/micc.12768] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 05/11/2022] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Necrotizing enterocolitis (NEC) is the most prevalent gastrointestinal emergency in premature infants and is characterized by a dysfunctional gut microcirculation. Therefore, there is a dire need for in vivo methods to characterize NEC-induced changes in the structure and function of the gut microcirculation, that is, its vascular phenotype. Since in vivo gut imaging methods are often slow and employ a single-contrast mechanism, we developed a rapid multicontrast imaging technique and a novel analyses pipeline for phenotyping the gut microcirculation. METHODS Using an experimental NEC model, we acquired in vivo images of the gut microvasculature and blood flow over a 5000 × 7000 μm2 field of view at 5 μm resolution via the following two endogenous contrast mechanisms: intrinsic optical signals and laser speckles. Next, we transformed intestinal images into rectilinear "flat maps," and delineated 1A/V gut microvessels and their perfusion territories as "intestinal vascular units" (IVUs). Employing IVUs, we quantified and visualized NEC-induced changes to the gut vascular phenotype. RESULTS In vivo imaging required 60-100 s per animal. Relative to the healthy gut, NEC intestines showed a significant overall decrease (i.e. 64-72%) in perfusion, accompanied by vasoconstriction (i.e. 9-12%) and a reduction in perfusion entropy (19%)within sections of the vascular bed. CONCLUSIONS Multicontrast imaging coupled with IVU-based in vivo vascular phenotyping is a powerful new tool for elucidating NEC pathogenesis.
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Affiliation(s)
- Janaka Senarathna
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Mark Kovler
- Department of Genetic MedicineThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of SurgeryThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Ayush Prasad
- Department of BiophysicsThe Johns Hopkins UniversityBaltimoreMarylandUSA
| | - Akanksha Bhargava
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Nitish V. Thakor
- Department of Biomedical EngineeringThe Johns Hopkins UniversityBaltimoreMarylandUSA
| | - Chhinder P. Sodhi
- Department of Genetic MedicineThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of SurgeryThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of Cell BiologyThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - David J. Hackam
- Department of Genetic MedicineThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of SurgeryThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of Cell BiologyThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Arvind P. Pathak
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of Biomedical EngineeringThe Johns Hopkins UniversityBaltimoreMarylandUSA,Department of OncologyThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA,Department of Sidney Kimmel Comprehensive Cancer CenterThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
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14
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Kalejahi BK, Meshgini S, Danishvar S, Khorram S. Diagnosis of liver disease by computer- assisted imaging techniques: A literature review. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-216379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician’s or doctor’s experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach.
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Affiliation(s)
- Behnam Kiani Kalejahi
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Saeed Meshgini
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sebelan Danishvar
- Department of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
| | - Sara Khorram
- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
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15
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Patel RK, Kashyap M. Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform. Biocybern Biomed Eng 2022; 42:829-841. [PMID: 35791429 PMCID: PMC9247116 DOI: 10.1016/j.bbe.2022.06.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 06/15/2022] [Accepted: 06/18/2022] [Indexed: 11/18/2022]
Abstract
The COVID-19 epidemic has been causing a global problem since December 2019. COVID-19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is essential. The progression of COVID-19 lung illness has been demonstrated to be aided by chest imaging. The respiratory system is the most vulnerable component of the human body to the COVID virus. COVID can be diagnosed promptly and accurately using images from a chest X-ray and a computed tomography scan. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist venous entry, and pinpoint any new heart problems. The traditional and trending tools are physical, time-inefficient, and not more accurate. Many techniques for detecting COVID utilizing CT scan images have recently been developed, yet none of them can efficiently detect COVID at an early stage. We proposed a two-dimensional Flexible analytical wavelet transform (FAWT) based on a novel technique in this work. This method is decomposed pre-processed images into sub-bands. Then statistical-based relevant features are extracted, and principal component analysis (PCA) is used to identify robust features. After that, robust features are ranked with the help of the Student's t-value algorithm. Finally, features are applied to Least Square-SVM (RBF) for classification. According to the experimental outcomes, our model beat state-of-the-art approaches for COVID classification. This model attained better classification accuracy of 93.47%, specificity 93.34%, sensitivity 93.6% and F1-score 0.93 using tenfold cross-validation.
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16
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PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5667264. [PMID: 35602611 PMCID: PMC9117073 DOI: 10.1155/2022/5667264] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/29/2022] [Indexed: 02/06/2023]
Abstract
Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process's 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.
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17
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Gudigar A, Raghavendra U, Samanth J, Dharmik C, Gangavarapu MR, Nayak K, Ciaccio EJ, Tan RS, Molinari F, Acharya UR. Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques. J Imaging 2022; 8:jimaging8040102. [PMID: 35448229 PMCID: PMC9030738 DOI: 10.3390/jimaging8040102] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/22/2022] [Accepted: 03/28/2022] [Indexed: 02/04/2023] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student’s t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100·log10(SigFea/2) in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (C.D.); (M.R.G.)
| | - U. Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (C.D.); (M.R.G.)
- Correspondence:
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India; (J.S.); (K.N.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (C.D.); (M.R.G.)
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (C.D.); (M.R.G.)
| | - Krishnananda Nayak
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India; (J.S.); (K.N.)
| | - Edward J. Ciaccio
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA;
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore;
- Duke-NUS Medical School, Singapore 169857, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Clementi, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 8608555, Japan
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
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18
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Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering. INFORMATION 2022. [DOI: 10.3390/info13030140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022] Open
Abstract
Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.
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19
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Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12010521] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.
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20
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Faust O, En Wei Koh J, Jahmunah V, Sabut S, Ciaccio EJ, Majid A, Ali A, Lip GYH, Acharya UR. Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8059. [PMID: 34360349 PMCID: PMC8345794 DOI: 10.3390/ijerph18158059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/05/2021] [Accepted: 07/23/2021] [Indexed: 11/18/2022]
Abstract
This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources.
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Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
| | - Joel En Wei Koh
- School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; (J.E.W.K.); (V.J.); (U.R.A.)
| | - Vicnesh Jahmunah
- School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; (J.E.W.K.); (V.J.); (U.R.A.)
| | - Sukant Sabut
- School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha 751024, India;
| | - Edward J. Ciaccio
- Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA;
| | - Arshad Majid
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK;
| | - Ali Ali
- Sheffield Teaching Hospitals NIHR Biomedical Research Centre, Sheffield S10 2JF, UK;
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L69 7TX, UK;
- Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, 9000 Aalborg, Denmark
| | - U. Rajendra Acharya
- School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; (J.E.W.K.); (V.J.); (U.R.A.)
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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21
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Maheshwari S, Sharma RR, Kumar M. LBP-based information assisted intelligent system for COVID-19 identification. Comput Biol Med 2021; 134:104453. [PMID: 33957343 PMCID: PMC8087862 DOI: 10.1016/j.compbiomed.2021.104453] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/19/2021] [Accepted: 04/24/2021] [Indexed: 01/08/2023]
Abstract
A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.
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Affiliation(s)
- Shishir Maheshwari
- Discipline of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, 333031, India.
| | - Rishi Raj Sharma
- Department of Electronics Engineering, Defence Institute of Advanced Technology, Pune, 411025, India.
| | - Mohit Kumar
- NAF Department, Indian Institute of Technology Kanpur, Kanpur, India.
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22
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Popa SL, Ismaiel A, Cristina P, Cristina M, Chiarioni G, David L, Dumitrascu DL. Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review. Diagnostics (Basel) 2021; 11:1078. [PMID: 34204822 PMCID: PMC8231502 DOI: 10.3390/diagnostics11061078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. METHODS PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). RESULTS Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. CONCLUSIONS We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.
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Affiliation(s)
- Stefan L. Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Pop Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Mogosan Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Giuseppe Chiarioni
- Division of Gastroenterology, University of Verona, 1-37126 AOUI Verona, Italy;
| | - Liliana David
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Dan L. Dumitrascu
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
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23
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Liver disease classification from ultrasound using multi-scale CNN. Int J Comput Assist Radiol Surg 2021; 16:1537-1548. [PMID: 34097226 DOI: 10.1007/s11548-021-02414-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/20/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. METHODS In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. RESULTS Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ([Formula: see text]). CONCLUSIONS Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.
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24
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Koh JEW, De Michele S, Sudarshan VK, Jahmunah V, Ciaccio EJ, Ooi CP, Gururajan R, Gururajan R, Oh SL, Lewis SK, Green PH, Bhagat G, Acharya UR. Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106010. [PMID: 33831693 DOI: 10.1016/j.cmpb.2021.106010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Celiac disease is an autoimmune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that are associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsy histopathology, endoscopy, and video capsule endoscopy (VCE) involve manual interpretation of photomicrographs or images, which can be time-consuming and difficult, with inter-observer variability. In this paper, a machine learning technique was developed for the automation of biopsy image analysis to detect and classify villous atrophy based on modified Marsh scores. This is one of the first studies to employ conventional machine learning to automate the use of biopsy images for celiac disease detection and classification. METHODS The Steerable Pyramid Transform (SPT) method was used to obtain sub bands from which various types of entropy and nonlinear features were computed. All extracted features were automatically classified into two-class and multi-class, using six classifiers. RESULTS An accuracy of 88.89%, was achieved for the classification of two-class villous abnormalities based on analysis of Hematoxylin and Eosin (H&E) stained biopsy images. Similarly, an accuracy of 82.92% was achieved for the two-class classification of red-green-blue (RGB) biopsy images. Also, an accuracy of 72% was achieved in the classification of multi-class biopsy images. CONCLUSION The results obtained are promising, and demonstrate the possibility of automating biopsy image interpretation using machine learning. This can assist pathologists in accelerating the diagnostic process without bias, resulting in greater accuracy, and ultimately, earlier access to treatment.
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Affiliation(s)
- Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Simona De Michele
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - Vidya K Sudarshan
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Edward J Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Raj Gururajan
- School of Business, University of Southern Queensland Springfield, Australia
| | | | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Suzanne K Lewis
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Peter H Green
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Govind Bhagat
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA; Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business, University of Southern Queensland Springfield, Australia; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan.
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25
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Das A, Connell M, Khetarpal S. Digital image analysis of ultrasound images using machine learning to diagnose pediatric nonalcoholic fatty liver disease. Clin Imaging 2021; 77:62-68. [PMID: 33647632 DOI: 10.1016/j.clinimag.2021.02.038] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 01/09/2021] [Accepted: 02/21/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Prevalence of nonalcoholic fatty liver disease (NAFLD) in children is rising with the epidemic of childhood obesity. Our objective was to perform digital image analysis (DIA) of ultrasound (US) images of the liver to develop a machine learning (ML) based classification model capable of differentiating NAFLD from healthy liver tissue and compare its performance with pixel intensity-based indices. METHODS De-identified hepatic US images obtained as part of a cross-sectional study examining pediatric NAFLD prevalence were used to build an image database. Texture features were extracted from a representative region of interest (ROI) selected from US images of subjects with normal liver and subjects with confirmed NAFLD using ImageJ and MAZDA image analysis software. Multiple ML classification algorithms were evaluated. RESULTS Four-hundred eighty-four ROIs from images in 93 normal subjects and 260 ROIs from images in 39 subjects with NAFLD with 28 texture features extracted from each ROI were used to develop, train, and internally validate the model. An ensembled ML model comprising Support Vector Machine, Neural Net, and Extreme Gradient Boost algorithms was accurate in differentiating NAFLD from normal when tested in an external validation cohort of 211 ROIs from images in 42 children. The texture-based ML model was also superior in predictive accuracy to ML models developed using the intensity-based indices (hepatic-renal index and the hepatic echo-intensity attenuation index). CONCLUSION ML-based predictive models can accurately classify NAFLD US images from normal liver images with high accuracy using texture analysis features.
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Affiliation(s)
- Amit Das
- Adjunct Research Associate, Valleywise Health Medical Center, Phoenix, AZ, United States of America.
| | - Mary Connell
- Department of Radiology, Valleywise Health Medical Center, Phoenix, AZ, United States of America
| | - Shailesh Khetarpal
- Department of Pediatrics, Valleywise Health Medical Center, Phoenix, AZ, United States of America
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Zamanian H, Mostaar A, Azadeh P, Ahmadi M. Implementation of Combinational Deep Learning Algorithm for Non-alcoholic Fatty Liver Classification in Ultrasound Images. J Biomed Phys Eng 2021; 11:73-84. [PMID: 33564642 PMCID: PMC7859380 DOI: 10.31661/jbpe.v0i0.2009-1180] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 10/23/2020] [Indexed: 12/12/2022]
Abstract
Background Nowadays, fatty liver is one of the commonly occurred diseases for the liver which can be observed generally in obese patients. Final results from a variety of exams and imaging methods can help to identify and evaluate people affected by this condition. Objective The aim of this study is to present a combined algorithm based on neural networks for the classification of ultrasound images from fatty liver affected patients. Material and Methods In experimental research can be categorized as a diagnostic study which focuses on classification of the acquired ultrasonography images for 55 patients with fatty liver. We implemented pre-trained convolutional neural networks of Inception-ResNetv2, GoogleNet, AlexNet, and ResNet101 to extract features from the images and after combining these resulted features, we provided support vector machine (SVM) algorithm to classify the liver images. Then the results are compared with the ones in implementing the algorithms independently. Results The area under the receiver operating characteristic curve (AUC) for the introduced combined network resulted in 0.9999, which is a better result compared to any of the other introduced algorithms. The resulted accuracy for the proposed network also caused 0.9864, which seems acceptable accuracy for clinical application. Conclusion The proposed network can be used with high accuracy to classify ultrasound images of the liver to normal or fatty. The presented approach besides the high AUC in comparison with other methods have the independence of the method from the user or expert interference.
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Affiliation(s)
- H Zamanian
- MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A Mostaar
- PhD, Department of Medical Physics and Biomedical Engineering and, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- PhD, Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - P Azadeh
- MD, Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - M Ahmadi
- PhD, Department of Medical Physics and Biomedical Engineering and, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Raghavendra U, Pham TH, Gudigar A, Vidhya V, Rao BN, Sabut S, Wei JKE, Ciaccio EJ, Acharya UR. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-020-00257-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
AbstractBrain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.
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Książek W, Hammad M, Pławiak P, Acharya UR, Tadeusiewicz R. Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection. Biocybern Biomed Eng 2020; 40:1512-1524. [DOI: 10.1016/j.bbe.2020.08.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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29
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Sezer A, Basri Sezer H. Convolutional neural network based diagnosis of bone pathologies of proximal humerus. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2018.11.115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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30
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Koh JEW, Raghavendra U, Gudigar A, Ping OC, Molinari F, Mishra S, Mathavan S, Raman R, Acharya UR. A novel hybrid approach for automated detection of retinal detachment using ultrasound images. Comput Biol Med 2020; 120:103704. [PMID: 32250849 DOI: 10.1016/j.compbiomed.2020.103704] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/12/2020] [Accepted: 03/12/2020] [Indexed: 12/18/2022]
Abstract
Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.
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Affiliation(s)
- Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ooi Chui Ping
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - Samarth Mishra
- ONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankarnethralaya campus, Chennai, 600006, India
| | - Sinnakaruppan Mathavan
- ONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankarnethralaya campus, Chennai, 600006, India
| | - Rajiv Raman
- ONGC Department of Genetics and Molecular Biology, Vision Research Foundation, Sankarnethralaya campus, Chennai, 600006, India
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Rajendra Acharya U, Meiburger KM, Faust O, En Wei Koh J, Lih Oh S, Ciaccio EJ, Subudhi A, Jahmunah V, Sabut S. Automatic detection of ischemic stroke using higher order spectra features in brain MRI images. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2019.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Rajendra Acharya U, Meiburger KM, Wei Koh JE, Vicnesh J, Ciaccio EJ, Shu Lih O, Tan SK, Aman RRAR, Molinari F, Ng KH. Automated plaque classification using computed tomography angiography and Gabor transformations. Artif Intell Med 2019; 100:101724. [PMID: 31607348 DOI: 10.1016/j.artmed.2019.101724] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/23/2019] [Accepted: 09/06/2019] [Indexed: 12/18/2022]
Abstract
Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients. The automated technique is based on various features that are extracted from the Gabor transform of the acquired CTA images. Specifically, seven features are extracted from the Gabor coefficients : energy, and Kapur, Max, Rényi, Shannon, Vajda, and Yager entropies. The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. Moreover, two well-known feature reduction techniques were employed, and the features acquired were also ranked according to F-value and input to several classifiers. The best classification results were obtained using all computed features without the employment of feature reduction, using a probabilistic neural network. An accuracy, positive predictive value, sensitivity, and specificity of 89.09%, 91.70%, 91.83% and 83.70% was obtained, respectively. Based on these results, it is evident that the technique can be helpful in the automated classification of plaques present in CTA images, and may become an important tool to reduce procedural costs and patient radiation dose. This could also aid clinicians in plaque diagnostics.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
| | - Kristen M Meiburger
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - Edward J Ciaccio
- Department of Medicine - Division of Cardiology, Columbia University, New York, USA
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Sock Keow Tan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Raja Rizal Azman Raja Aman
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Nayak DR, Dash R, Majhi B, Acharya UR. Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities. Comput Med Imaging Graph 2019; 77:101656. [PMID: 31563069 DOI: 10.1016/j.compmedimag.2019.101656] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/11/2019] [Accepted: 08/22/2019] [Indexed: 12/18/2022]
Abstract
Binary classification of brain magnetic resonance (MR) images has made remarkable progress and many automated systems have been developed in the last decade. Multiclass classification of brain MR images is comparatively more challenging and has great clinical significance. Hence, it has recently become an active area of research in biomedical image processing. In this paper, an automated multiclass brain MR classification framework is proposed to categorize the MR images into five classes such as brain stroke, degenerative disease, infectious disease, brain tumor, and normal brain. A texture based feature descriptor is proposed using curvelet transform and Tsallis entropy to extract salient features from MR images. The potential of Tsallis entropy features is compared with Shannon entropy features. A kernel extension of random vector functional link network (KRVFL) is used to perform multiclass classification and improve the generalization performance at faster training speed. To validate the proposed method, two standard multiclass brain MR datasets (MD-1 and MD-2) are used. The proposed system obtained classification accuracies of 97.33% and 94.00% for MD-1 and MD-2 datasets respectively using 5-fold cross validation approach. The experimental results demonstrated the effectiveness of our system compared to the state-of-the-art schemes and hence, can be utilized as a supportive tool by physicians to verify their screening.
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Affiliation(s)
- Deepak Ranjan Nayak
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram 600127, India.
| | - Ratnakar Dash
- Department of Computer Science and Engineering, National Institute of Technology Rourkela, 769008, India.
| | - Banshidhar Majhi
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram 600127, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Malaysia.
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Investigation of Fusion Features for Apple Classification in Smart Manufacturing. Symmetry (Basel) 2019. [DOI: 10.3390/sym11101194] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.
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35
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Chen CI, Chen TB, Lu NH, Du WC, Liang CY, Liu KI, Hsu SY, Lin LW, Huang YH. Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study. Proc Inst Mech Eng H 2019; 233:1100-1112. [PMID: 31441386 DOI: 10.1177/0954411919871123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients' effective liver ultrasound images-30 normal, 44 fatty, and 40 cancerous-were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method-75.0%, 0.548, and 0.280-or those of the support vector machine method-75.7%, 0.637, and 0.293-respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.
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Affiliation(s)
- Chih-I Chen
- Department of Information Engineering, I-Shou University, Kaohsiung.,Division of Colon & Rectal Surgery, Department of Surgery, E-Da Hospital, I-Shou University, Kaohsiung
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung.,Department of Radiology, E-Da Hospital, I-Shou University, Kaohsiung
| | - Wei-Chang Du
- Department of Information Engineering, I-Shou University, Kaohsiung
| | - Chih-Yu Liang
- Department of Information Engineering, I-Shou University, Kaohsiung.,Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung.,Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung
| | - Ko-Ing Liu
- Department of Radiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung
| | - Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, Kaohsiung
| | - Li Wei Lin
- The School of Chinese Medicine for Post-Baccalaureate, I-Shou University, Kaohsiung
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung
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Gudigar A, Raghavendra U, Devasia T, Nayak K, Danish SM, Kamath G, Samanth J, Pai UM, Nayak V, Tan RS, Ciaccio EJ, Acharya UR. Global weighted LBP based entropy features for the assessment of pulmonary hypertension. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.03.027] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Rajalakshmi T, Snekhalatha U, Baby J. SEGMENTATION OF LIVER TUMOR USING FAST GREEDY SNAKE ALGORITHM. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2019. [DOI: 10.4015/s1016237219500133] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Back Ground: Liver tumors are a type of growth found in the liver which can be categorized as malignant or benign. It is also called as hepatic tumors. Early stage detection of tumor could be treated at a faster phase; if it is left undiagnosed it may lead to several complications. Traditional method adopted for diagnosis can be time consuming, error-prone and also requires an experts study. Hence a non invasive diagnostic method is required which overcomes the flaws of conventional method. Liver segmentation from CT images in post processing techniques not only is an essential prerequisite, but, by playing an important role in confirming liver function, pathological, and anatomical studies, is also a key technique for diagnosis of liver disease. Hence in the proposed study Fast greedy snakes algorithm in abdominal CT images were used for segmenting tumor portion. Aim & Objectives: The aim and objectives of study is: (i) to segment tumor region in the liver image using Fast Greedy Snakes Algorithm (FGSA); (ii) to extract the GLCM features from the segmented region; (iii) to classify the normal and abnormal liver image using neural network classifier. Methodology: The study involved a total of 30 normal and 30 abnormal Images from database. In the proposed study automated segmentation was performed using Fast Greedy Snakes (FGS) Algorithm and the features were extracted using GLCM method. Classification of normal and abnormal images was carried out using Back propagation Neural Network classifier. Result: The proposed FGS algorithm provides accurate segmentation in liver images. Statistical features like mean, kurtosis, correlation and Entropy showed a higher value for the normal image than liver tumor image. On the other hand, features like Skewness, Homogeneity, contrast, Energy and standard deviation showed a comparatively higher value for a liver tumor image than the normal. Statistical features such as Mean, Contrast, Homogeneity and standard deviation are statistically significant at [Formula: see text]. Features like correlation, entropy and energy exhibits significance at [Formula: see text]. The feature extracted values provided significant difference between the normal and abnormal liver images. The neural network classifier yields the sensitivity of 95.8%, sensitivity of 81.4% and achieved the overall accuracy of 92%. Conclusion: A most accurate, reliable and fast automated method was implemented to segment the liver tumor image using Fast Greedy snakes algorithm. Hence the proposed algorithm resulted in effective segmentation and the classifier could classify the normal and abnormal images with greater accuracy.
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Affiliation(s)
- T. Rajalakshmi
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, Tamil Nadu, India
| | - U. Snekhalatha
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, Tamil Nadu, India
| | - Jisha Baby
- Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, Tamil Nadu, India
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Kriti, Virmani J, Agarwal R. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Nishida N, Yamakawa M, Shiina T, Kudo M. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 2019; 13:416-421. [PMID: 30790230 DOI: 10.1007/s12072-019-09937-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 02/02/2019] [Indexed: 12/13/2022]
Abstract
An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 337-2 Ohno-higashi, Osaka-sayama, Osaka, 589-8511, Japan.
| | - Makoto Yamakawa
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Shiina
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 337-2 Ohno-higashi, Osaka-sayama, Osaka, 589-8511, Japan
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Maheshwari S, Kanhangad V, Pachori RB, Bhandary SV, Acharya UR. Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Comput Biol Med 2019; 105:72-80. [DOI: 10.1016/j.compbiomed.2018.11.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/18/2022]
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Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04025-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Koh JEW, Hagiwara Y, Oh SL, Tan JH, Ciaccio EJ, Green PH, Lewis SK, Rajendra Acharya U. Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images. FUTURE GENERATION COMPUTER SYSTEMS 2019; 90:86-93. [DOI: 10.1016/j.future.2018.07.044] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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Acharya UR, Raghavendra U, Koh JEW, Meiburger KM, Ciaccio EJ, Hagiwara Y, Molinari F, Leong WL, Vijayananthan A, Yaakup NA, Fabell MKBM, Yeong CH. Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 166:91-98. [PMID: 30415722 DOI: 10.1016/j.cmpb.2018.10.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 08/24/2018] [Accepted: 10/01/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images. METHODS The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis. RESULTS Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis. CONCLUSIONS The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore
| | - Kristen M Meiburger
- Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy.
| | - Edward J Ciaccio
- Department of Medicine, Columbia University, New York, NY, 10032, USA
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy
| | - Wai Ling Leong
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Anushya Vijayananthan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nur Adura Yaakup
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Mohd Kamil Bin Mohd Fabell
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Chai Hong Yeong
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia
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Hagiwara Y, Koh JEW, Tan JH, Bhandary SV, Laude A, Ciaccio EJ, Tong L, Acharya UR. Computer-aided diagnosis of glaucoma using fundus images: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 165:1-12. [PMID: 30337064 DOI: 10.1016/j.cmpb.2018.07.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/02/2018] [Accepted: 07/25/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective. METHODS The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma. RESULTS The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. CONCLUSIONS Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.
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Affiliation(s)
- Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Jen Hong Tan
- National University of Singapore, Institute of System Science
| | | | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | - Louis Tong
- Ocular Surface Research Group, Singapore Eye Research Institute, Singapore; Cornea and External Eye Disease Service, Singapore National Eye Center, Singapore; Eye Academic Clinical Program, Duke-NUS Medical School, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia.
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Song K, Bi JH, Qiu ZW, Felizardo R, Girard L, Minna JD, Gazdar AF. A quantitative method for assessing smoke associated molecular damage in lung cancers. Transl Lung Cancer Res 2018; 7:439-449. [PMID: 30225209 PMCID: PMC6131178 DOI: 10.21037/tlcr.2018.07.01] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 06/28/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND While tobacco exposure is the cause of the vast majority of lung cancers, an important percentage arise in lifetime never smokers. Documenting the precise extent of tobacco induced molecular changes may be of importance. Also, the contribution of environmental tobacco smoke (ETS) is difficult to assess. METHODS We developed and validated a quantitative method to assess the extent of tobacco related molecular damage by combing the most characteristic changes associated with tobacco smoke, the tumor mutation burden (TMB) and type of molecular changes present in lung cancers. Using maximum entropy (MaxEnt) as a classifier, we developed a F score. F score values >0 were considered to show evidence of tobacco related molecular damage, while values ≤0 were considered to lack evidence of tobacco related molecular damage. Compared to the stated patient tobacco exposure histories, the F scores had sensitivity, specificity and accuracy values of 85-87%. Using this method, we analyzed public data sets of lung adenocarcinoma (LUAD), lung squamous cell (LUSC) and small cell lung cancer (SCLC). RESULTS Less than 10% of LUSCs and SCLCs had negative F scores, while 27% to 35% of LUADs had positive scores. The F score showed a highly significant downward trend when LUADs were subdivided into the following categories: ever, reformed ≤15 years, reformed >15 years and never smokers. Most of the examined bronchial carcinoids (a lung cancer type not associated with smoke exposure) had negative F scores. In addition, most LUADs with EGFR mutations had negative F scores, while almost all with KRAS mutations had positive scores. CONCLUSIONS We have established and validated a quantitative assay that will be of use in assessing the presence and degree of smoke associated molecular damage in lung cancers arising in ever and never smokers.
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Affiliation(s)
- Kai Song
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA
| | - Jia-Hao Bi
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Zhe-Wei Qiu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Rui Felizardo
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Luc Girard
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA
- Departments of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
| | - John D. Minna
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA
- Departments of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
- Departments of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Adi F. Gazdar
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA
- Departments of Pathology, UT Southwestern Medical Center, Dallas, TX, USA
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Zhang J, Li J, Ma J, Wang H, Yi Y. Human fibroblast growth factor-21 serves as a predictor and prognostic factor in patients with hepatitis B cirrhosis combined with adrenal insufficiency. Exp Ther Med 2018; 15:3189-3196. [PMID: 29545834 PMCID: PMC5841067 DOI: 10.3892/etm.2018.5840] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 07/27/2017] [Indexed: 12/11/2022] Open
Abstract
Hepatitis B cirrhosis is caused by liver cell necrosis, residual liver cell nodular regeneration, connective tissue hyperplasia and fiber formation, which frequently leads to adrenal insufficiency. Previous reports have demonstrated that human fibroblast growth factor (hFGF)-21 is a multifunctional protein that exhibits potential therapeutic value for metabolic diseases. The present study investigated the diagnostic value of hFGF-21 and analyzed the potential molecular mechanism in the progression of hepatitis B cirrhosis combined with adrenal insufficiency. Characteristics of cellular immunity and humoral immunity were analyzed in patients with hepatitis B cirrhosis combined with adrenal insufficiency (PhbA). Results demonstrated that expression levels of hFGF-21 were downregulated in plasma and liver cells isolated from clinical specimens. Plasma concentration levels of hFGF-21 were upregulated in prognostic PhbA. In vitro assays indicated that hFGF-21 treatment decreased the continuous deposition of extracellular matrix and reactive oxygen species in liver cells isolated from clinical specimens. Results also demonstrated that hFGF-21 treatment downregulated inflammatory cytokines. It was observed that hFGF-21 treatment downregulated nuclear factor (NF)-κB and Kruppel-like factor 6. Notably, transforming growth factor (TGF)-β, platelet-derived growth factor and epidermal growth factor levels were improved by hFGF-21 treatment. In conclusion, these results indicated that hFGF-21 inhibits inflammation by regulation of the NF-κB-mediated TGF-β signaling pathway, which may serve as a predictor and prognostic factor in PhbA.
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Affiliation(s)
- Jian Zhang
- Emergency Department, Beijing You'an Hospital, Capital Medical University, Beijing 100069, P.R. China
| | - Junhong Li
- Emergency Department, Beijing You'an Hospital, Capital Medical University, Beijing 100069, P.R. China
| | - Junwei Ma
- Emergency Department, Beijing You'an Hospital, Capital Medical University, Beijing 100069, P.R. China
| | - Hongxin Wang
- Emergency Department, Beijing You'an Hospital, Capital Medical University, Beijing 100069, P.R. China
| | - Yin Yi
- Emergency Department, Beijing You'an Hospital, Capital Medical University, Beijing 100069, P.R. China
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Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Comput Biol Med 2018; 94:11-18. [PMID: 29353161 DOI: 10.1016/j.compbiomed.2017.12.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 12/29/2017] [Accepted: 12/29/2017] [Indexed: 12/12/2022]
Abstract
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.
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Bharath R, Rajalakshmi P, Mateen MA. Multi-modal framework for automatic detection of diagnostically important regions in nonalcoholic fatty liver ultrasonic images. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Acharya UR, Hagiwara Y, Koh JEW, Oh SL, Tan JH, Adam M, Tan RS. Entropies for automated detection of coronary artery disease using ECG signals: A review. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.03.001] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Acharya UR, Hagiwara Y, Sudarshan VK, Chan WY, Ng KH. Towards precision medicine: from quantitative imaging to radiomics. J Zhejiang Univ Sci B 2018; 19:6-24. [PMID: 29308604 PMCID: PMC5802973 DOI: 10.1631/jzus.b1700260] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/16/2017] [Indexed: 12/12/2022]
Abstract
Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.
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Affiliation(s)
- U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Vidya K. Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
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