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Cited by in CrossRef
For: de Oliveira JEE, Araújo AA, Deserno TM. Content-based image retrieval applied to BI-RADS tissue classification in screening mammography. World J Radiol 2011; 3(1): 24-31 [PMID: 21286492 DOI: 10.4329/wjr.v3.i1.24]
URL: https://www.wjgnet.com/1949-8470/full/v3/i1/24.htm
Number Citing Articles
1
Rajeshwari S. Patil, Nagashettappa Biradar. Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifierInternational Journal of Intelligent Computing and Cybernetics 2020; 13(2) doi: 10.1108/IJICC-10-2019-0116
2
Abdelali Elmoufidi, Khalid El Fahssi, Said Jai‐andaloussi, Abderrahim Sekkaki, Quellec Gwenole, Mathieu Lamard. Anomaly classification in digital mammography based on multiple‐instance learningIET Image Processing 2018; 12(3) doi: 10.1049/iet-ipr.2017.0536
3
Gwenole Quellec, Mathieu Lamard, Michel Cozic, Gouenou Coatrieux, Guy Cazuguel. Multiple-Instance Learning for Anomaly Detection in Digital MammographyIEEE Transactions on Medical Imaging 2016; 35(7) doi: 10.1109/TMI.2016.2521442
4
Jyotismita Chaki, Nilanjan Dey. Data Tagging in Medical Images: A Survey of the State-of-ArtCurrent Medical Imaging Formerly Current Medical Imaging Reviews 2021; 16(10) doi: 10.2174/1573405616666200218130043
5
Shaila Chugh, Saurabh Sharma, Sunil Phulre, M. Zahid Alam, Shubham Sharma, Manish Rai, Sanjay Kumar Tehariya. Hybrid mammogram mass segmentation and classification using efficient handcrafted features and ensemble learningDiscover Computing 2026; 29(1) doi: 10.1007/s10791-026-10387-4
6
Reza Majidpourkhoei, Mehdi Alilou, Kambiz Majidzadeh, Amin Babazadehsangar. A novel deep learning framework for lung nodule detection in 3d CT imagesMultimedia Tools and Applications 2021; 80(20) doi: 10.1007/s11042-021-11066-w
7
Shubhi Sharma, Pritee Khanna. Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVMJournal of Digital Imaging 2015; 28(1) doi: 10.1007/s10278-014-9719-7
8
Aditya A. Shastri, Deepti Tamrakar, Kapil Ahuja. Density-wise two stage mammogram classification using texture exploiting descriptorsExpert Systems with Applications 2018; 99 doi: 10.1016/j.eswa.2018.01.024
9
Thomas Christy Bobby, Swaminathan Ramakrishnan. Swarm, Evolutionary, and Memetic ComputingLecture Notes in Computer Science 2012; 7677 doi: 10.1007/978-3-642-35380-2_69
10
D. Saranyaraj, R. Vaisshale, R. NandhaKishore. International Conference on Innovative Computing and CommunicationsLecture Notes in Networks and Systems 2023; 703 doi: 10.1007/978-981-99-3315-0_18
11
Luiz A. P. Neves, Gilson A. Giraldi. Topics in Medical Image Processing and Computational VisionLecture Notes in Computational Vision and Biomechanics 2013; 8 doi: 10.1007/978-94-007-0726-9_3
12
Xiaorong Li, Yunliang Qi, Meng Lou, Wenwei Zhao, Jie Meng, Wenjun Zhang, Yide Ma. Breast density measurement methods on mammograms: a reviewMultimedia Systems 2022; 28(6) doi: 10.1007/s00530-022-00955-1
13
Thomas Deserno, Michael Soiron, Julia Oliveira, Arnaldo Araujo. Towards Computer-Aided Diagnostics of Screening Mammography Using Content-Based Image Retrieval2011 24th SIBGRAPI Conference on Graphics, Patterns and Images 2011;  doi: 10.1109/SIBGRAPI.2011.40
14
Siti Salmah Yasiran, Shaharuddin Salleh, Rozi Mahmud. Haralick texture and invariant moments features for breast cancer classification2016; 1750 doi: 10.1063/1.4954535
15
Fatima Ghazi, Aziza Benkuider, Mohamed Zraidi, Fouad Ayoub, Khalil Ibrahimi. Neighborhood Feature Extraction and Haralick Attributes for Medical Image Analysis: Application to Breast Cancer Mammography Image2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM) 2023;  doi: 10.1109/WINCOM59760.2023.10323028
16
D Saranyaraj. Image De-noising and Edge Segmentation using Bilateral Filtering and Gabor-cut for Edge Representation of a Breast Tumor2022 International Conference on Engineering and Emerging Technologies (ICEET) 2022;  doi: 10.1109/ICEET56468.2022.10007228
17
Omid Rahmani Seryasat, Javad Haddadnia. Evaluation of a New Ensemble Learning Framework for Mass Classification in MammogramsClinical Breast Cancer 2018; 18(3) doi: 10.1016/j.clbc.2017.05.009
18
Xiaonan Gong, Zhen Yang, Deyuan Wang, Yunliang Qi, Yanan Guo, Yide Ma. Breast density analysis based on glandular tissue segmentation and mixed feature extractionMultimedia Tools and Applications 2019; 78(22) doi: 10.1007/s11042-019-07917-2
19
Keith Chikamai, Serestina Viriri, Jules-Raymond Tapamo. Mammogram content-based image retrieval based on malignancy classificationIntelligent Data Analysis 2017; 21(5) doi: 10.3233/IDA-163101
20
Samaneh Aminikhanghahi, Sung Shin, Wei Wang, Soon I. Jeon, Seong H. Son. A new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classificationMultimedia Tools and Applications 2017; 76(7) doi: 10.1007/s11042-016-3605-x
21
C. Mata, A. Oliver, A. Lalande, P. Walker, J. Martí. On the Use of XML in Medical Imaging Web-Based ApplicationsIRBM 2017; 38(1) doi: 10.1016/j.irbm.2016.10.001
22
Fatima Ghazi, Aziza Benkuider, Fouad Ayoub, Khalil Ibrahimi. Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography ImageBioMedInformatics 2024; 4(2) doi: 10.3390/biomedinformatics4020066