Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination

Table 1 Convolutional neural network architectures adapted for medical image analysis

Ref.	Architecture	Key strengths	Limitations	Applications
[39]	AlexNet	Introduced rectified linear unit, dropout, graphics processing unit acceleration	Overfitting on small datasets	Histopathology image classification
[40]	VGGNet	Uniform structure, easy to implement	Large number of parameters, memory-intensive	Polyp detection, organ segmentation
[41]	GoogLeNet	Multi-scale feature extraction, fewer parameters than VGG	Complex architecture, harder to modify	Lesion classification, colonoscopy image analysis
[42]	ResNet	Residual connections solve vanishing gradient	Can overfit if dataset is small	Detection of GI tumors, segmentation of ulcers
[43]	U-Net	Excellent for biomedical segmentation, works with few images	Limited to segmentation tasks	Polyp segmentation, mucosal layer delineation
[44]	DenseNet	Strong gradient flow, parameter-efficient	Computationally intensive	Endoscopic image classification, disease grading
[45]	Attention U-Net	Incorporates attention for better focus on relevant regions	Increased complexity and longer training time	IBD severity scoring; small bowel bleeding detection
[46]	EfficientNet	Optimized trade-off between accuracy and speed	Requires careful scaling and tuning	Lightweight, mobile-compatible GI image classification
[47]	Swin-CNN	Window-based attention + CNN; balances local and global features	Complex design; tuning is more demanding	GI endoscopy video anomaly detection; GI tumor recognition
[48]	TransUNet	Combines CNN for feature extraction and Transformer for context modeling	Resource intensive; slower training	Computed tomography/magnetic resonance imaging organ segmentation; GI lesion boundary detection
[49]	MedT (medical transformer)	Pure transformer-based; excellent at long-range dependency modeling	Not optimal for small datasets; data hungry	Intestinal lesion segmentation; colorectal cancer prediction
[50]	ConvNeXt	Combines CNN stability with Transformer-like design; efficient training	Relatively new; limited ecosystem maturity	Multi-organ classification; tumor region detection

CNN: Convolutional neural network; GI: Gastrointestinal; IBD: Inflammatory bowel disease.

Table 2 Application of convolutional neural networks for lesion detection and classification in gastrointestinal diseases

Data	Architecture	Application	Key findings	Ref.
Endoscopic images	Deep CNN with high-resolution endoscopic input	Aids in detecting laterally spreading tumors in the colon using deep learning to enhance endoscopic visualization accuracy	The CNN model significantly improved the detection rate of laterally spreading tumors compared to conventional endoscopy, reducing oversight and potentially improving early colorectal cancer diagnosis. Accuracy increased notably in identifying flat lesions that are otherwise difficult to detect manually	[64]
GI endoscopy images	Deep CNN trained on large annotated image dataset	Designed to detect and classify a wide range of GI tract diseases in real-time using endoscopic imagery	The model achieved high diagnostic accuracy across multiple disease categories, outperforming traditional classifiers. It demonstrated strong potential for integration into clinical decision support tools, increasing consistency and efficiency in endoscopic diagnoses	[65]
Wireless capsule endoscopy images	CNN with real-time image processing pipeline	Supports non-invasive detection of GI diseases using capsule endoscopy video sequences	Achieved expert-level accuracy in detecting abnormal lesions in small bowel images. This system allows for automated and accurate screening, significantly reducing time spent by clinicians reviewing lengthy capsule footage	[66]
White-light endoscopy	CNN model trained on early gastric cancer cases	Early detection of gastric cancer through white-light endoscopy imagery with AI assistance	The model enabled early cancer identification with high sensitivity and specificity, improving upon the detection rates of endoscopists. Its implementation could lead to reduced gastric cancer mortality by facilitating prompt treatment decisions	[67]
Endoscopic images	CNN with multi-center training data	Detecting Helicobacter pylori infection from endoscopy images	The CNN demonstrated high performance and generalizability across institutions. It offers a non-invasive, fast diagnostic method, comparable to biopsy confirmation, enabling real-time feedback during endoscopic exams	[68]
Gastroscopy images	Deep dense CNN with channel attention mechanism	Automated classification of esophageal disease types (e.g., ulcers, neoplasms)	By incorporating attention modules, the model excelled in distinguishing subtle texture differences among esophageal pathologies, showing great promise in supporting less experienced endoscopists	[58]
Capsule endoscopy images	CNN for object detection	Automatic detection of small intestinal hookworms	The proposed system achieved high precision in detecting parasitic lesions, helping clinicians quickly identify the presence of hookworm infections, which are often missed due to subtle image features	[69]
Capsule endoscopy images	CNN trained on hemorrhagic potential lesions	Identification and differentiation of small bowel lesions with bleeding risk	Accurately categorized lesions by bleeding risk, offering clinicians critical insight for prioritizing cases. The system reduces subjectivity in evaluating lesion severity	[70]
Endoscopic images	Deep CNN classifier for esophageal lesion types	Differentiation of protruding esophageal lesions (e.g., GI stromal tumors, leiomyomas)	CNN provided more accurate differential diagnosis than generalist endoscopists. Can support pre-biopsy decision-making, potentially reducing unnecessary interventions	[71]
Endoscopic images	Transfer CNN architecture (e.g., ResNet-based)	Detection of early gastric cancer	Transfer learning from pre-trained CNNs enabled rapid training on limited labeled data while maintaining strong detection performance. Highlights usefulness of transfer learning in GI imaging where large datasets are rare	[72]
Endoscopic images	Fusion of multiple CNNs via fuzzy Minkowski distance	Multi-class classification of GI disorders	This fuzzy fusion method significantly boosted CNN decision reliability in classifying various GI tract diseases. Particularly effective in handling noisy or borderline cases by leveraging ensemble confidence	[73]
Double-balloon enteroscopy	Custom deep CNN with lesion region focusing	Detecting and classifying small bowel lesions (e.g., ulcers, tumors) during double-balloon enteroscopy	Achieved high sensitivity for subtle and rare lesions in the small intestine. Helped reduce diagnostic time and enabled detection of hard-to-access areas, improving double-balloon enteroscopy clinical utility	[74]
Endoscopic images	CNN with transfer learning (e.g., ResNet pretrained on ImageNet)	Detecting GI abnormalities in multi-center datasets	Transfer learning approach enhanced classification accuracy with limited training data. It proved robust across image variations, useful in practical endoscopy deployments with different imaging devices	[75]
GI tract images	Deep CNN with region proposal and hierarchical labeling	Classification of syndromes in the GI tract (e.g., inflammation, bleeding, neoplasms)	Outperformed traditional classifiers with over 90% accuracy on several syndrome categories. System demonstrated early signs of suitability for autonomous decision-making in AI-assisted endoscopy	[76]
Capsule endoscopy images	CNN with spatial attention and region localization	Detection and precise localization of GI diseases in capsule footage	The model not only identified lesions but also marked their spatial location, reducing time required for manual review. Showed high generalization across bleeding, polyps, and ulcers	[53]
Endoscopic images	Multi-fusion CNN with spatial and feature fusion	Diagnosis of complex GI disease states through multiple feature paths	Fusion approach combined texture, edge, and color patterns from multiple CNN streams, improving classification of ambiguous or compound lesions. Enabled more granular diagnosis	[77]
Capsule endoscopy images	Deep multi-class CNN classifier	Distinguishing ulcerative colitis, polyps, and dyed-lifted polyps	The model achieved strong performance on multi-label lesion classification, aiding clinicians in identifying inflammation patterns vs neoplastic changes from wireless capsule endoscopy images	[56]
Endoscopic images	Deep CNN for histological prediction	Detection of chronic atrophic gastritis	The system outperformed general practitioners in identifying chronic atrophic gastritis on white-light images. This could enable earlier intervention for patients at risk of gastric cancer	[78]
Endoscopic images	CNN-based diagnostic prediction model	Classification of multiple GI disorders including gastritis and neoplasms	The system offered a probabilistic diagnosis output that correlated well with clinical reports. Could serve as a second reader to reduce misdiagnosis	[79]
Endoscopic images	Deep CNN trained on villous atrophy cases	Detection of duodenal villous atrophy, a hallmark of celiac disease	Model enabled accurate identification of atrophic mucosal features that are often missed by the naked eye. Can improve non-invasive screening for celiac disease	[80]
Endoscopic images	CNN with lesion localization layer	Early gastric cancer classification and localization	The system not only detected cancer but pinpointed lesion boundaries, assisting in targeted biopsies. Shown to reduce oversight in flat lesions	[81]
Endoscopic images	Deep CNN prospectively tested	Diagnosis of chronic atrophic gastritis	Prospective validation showed significant improvement in diagnosis rate compared to conventional methods. Demonstrated practical deployment readiness	[82]
Endoscopic images	CNN trained and validated against multiple architectures	Detection of gastric mucosal lesions	Compared multiple CNN methods and showed the proposed model performed best in both sensitivity and specificity. Suggested a robust preprocessing pipeline	[83]
Endoscopic images	Deep CNN with classification and detection modules	Broad GI disease detection and classification, including ulcers and tumors	The CNN model integrated multiple diagnostic modules, enabling both lesion classification and visual confirmation. It significantly outperformed traditional rule-based systems and provided explainable outputs to support clinical decisions	[84]
Endoscopic images	Deep CNN trained on annotated gastric polyp datasets	Detection of gastric polyps with high sensitivity	The model effectively identified polyps, including flat and small ones. It helped reduce miss rates in routine gastroscopy and can act as a real-time assistant for novice endoscopists	[54]
Endoscopic images	Ensemble machine learning with CNN and decision tree comparisons	GI disease classification from diverse image types	Though primarily a comparative study, CNNs were shown to outperform other models in most lesion categories. Highlighted CNNs’ suitability for complex image recognition tasks in GI settings	[85]
Endoscopic images	Deep convolutional architecture trained on expert-labeled data	Diagnosis of common gastric lesions including gastritis and erosions	Demonstrated high diagnostic accuracy and reduced inter-observer variability, suggesting CNN use can help standardize endoscopic assessments across institutions	[86]
Capsule endoscopy	Deep CNN with binary output mode	Binary lesion detection (normal vs abnormal) in capsule endoscopy	The binary CNN model achieved excellent sensitivity in identifying abnormal frames, acting as an efficient pre-filter to aid clinicians reviewing thousands of capsule images	[87]
Endoscopic images	Deep CNN tested across multiple centers	Gastritis classification using AI comparable to gastroenterologists	Achieved expert-level performance in classifying gastritis subtypes. Multicenter validation proved its robustness across varied endoscopic systems and clinical environments	[20]
Endoscopic images	Attention-guided CNN with feature weighting	GI disease classification enhanced with attention mechanisms	Attention modules enabled the model to focus on key lesion areas, improving classification accuracy for overlapping or complex cases. Boosted explainability in predictions	[62]
Wireless capsule endoscopy images	Pretrained CNN fine-tuned on wireless capsule endoscopy images	GI tract disease classification from capsule endoscopy	The transfer learning-based CNN adapted well to wireless capsule endoscopy image features. It accurately classified disease regions, even under motion blur and illumination changes	[88]
Endoscopic images	GI-Net: CNN with anomaly detection capability	Multi-label classification of anomalies in GI tract	GI-Net offered near real-time classification across numerous disease types, showing strong performance in clinical pilot tests. Especially useful in large-scale screenings	[89]
Endoscopic images	Deep CNN trained on Helicobacter pylori-positive and negative images	Evaluation of Helicobacter pylori infection	Model achieved diagnostic accuracy comparable to experienced gastroenterologists. Could be integrated into live endoscopy for on-the-spot infection prediction	[90]
Gastric endoscopic images	Deep CNN optimized for malignancy detection	Classification of gastric malignancies	CNN showed excellent discrimination between benign and malignant lesions. Real-time feedback capability demonstrated potential for improving early gastric cancer outcomes	[91]
Endoscopic images	CNN for cancer detection trained on large datasets	Automatic gastric cancer detection from endoscopy	High sensitivity and specificity achieved. Could act as an AI “second observer” for difficult-to-see lesions during live endoscopy	[92]
Endoscopic images	Deep learning model with Helicobacter pylori-specific feature extraction	Helicobacter pylori diagnosis using AI interpretation of mucosal patterns	Enabled real-time assessment of infection risk without biopsy, saving time and cost. Also reduced patient discomfort by avoiding invasive tests	[93]
GI tract images	Ensemble of deep CNN + texture feature classifiers	GI abnormality detection from diverse image types	Fusion of deep and handcrafted features improved robustness in challenging image conditions. Showed enhanced adaptability across multiple GI lesion types	[94]
Capsule endoscopy images	CNN tailored for parasitic structure detection	Hookworm detection in small bowel	Model achieved very high detection rate and reduced review time. Critical in endemic regions where hookworm infections are often overlooked	[95]
Endoscopic images	CNN optimized for mucosal pattern recognition	Diagnosis of Helicobacter pylori infection based on endoscopic images	Enhanced early infection detection through image-only methods. Potentially replaces biopsy in low-resource settings	[96]
Endoscopic images	CNN using hierarchical feature fusion	Lesion detection in GI endoscopy	Feature fusion helped distinguish similar lesions, improving accuracy. Suitable for deployment in routine screening environments	[97]
Endoscopic images	Deep CNN with blind-spot awareness	Early gastric cancer detection in real-time	The model avoided blind spots, a common issue in manual diagnosis, reducing miss rate for flat and depressed lesions. Demonstrated strong potential for real-time applications	[98]
Capsule endoscopy images	Neural network with small-bowel angiectasia training	Detection of GI angiectasia	Achieved accurate identification of angiectasias with high sensitivity. Provided a non-invasive method to detect obscure GI bleeding causes, potentially reducing missed diagnoses during manual video review	[99]
Capsule endoscopy images	CNN-based ulcer detection system	Automated ulcer detection in small bowel	The CNN model effectively distinguished ulcers from normal mucosa, even in poor lighting or with capsule motion. Enhanced early diagnosis of Crohn’s disease and non-steroidal anti-inflammatory drug-induced ulcers	[100]
Endoscopic images	CNN architecture tailored for Helicobacter pylori features	Evaluation of Helicobacter pylori infection status	The model achieved performance comparable to histology-based diagnosis. Useful for non-invasive, in-procedure assessment, aiding in same-visit treatment planning	[101]
Endoscopic images	CNN trained on gastric tumor categories	Gastric neoplasm classification (e.g., adenomas, carcinomas)	Outperformed junior endoscopists in differentiating benign vs malignant neoplasms. Accelerated workflow and supported biopsy decision-making	[102]
Endoscopic images	Deep learning classifier with ensemble optimization	GI disease recognition in endoscopic images	Handled a wide range of disease types with strong classification performance. Showed potential for integration into automated report generation systems	[103]
Capsule endoscopy images	Deep CNN with binary detection of ulceration	Detection of erosions and ulcers	Demonstrated over 90% accuracy in detecting clinically relevant ulcerative lesions. Reduced time spent reviewing wireless capsule endoscopy footage by half	[104]
Biopsy images	Deep CNN for histopathological image classification	Detection of diseases (e.g., gastritis, carcinoma) in GI biopsy samples	Model classified histopathological lesions with high fidelity. Useful in screening large volumes of slides, especially in resource-constrained pathology labs	[105]
Endoscopic images	Deep CNN architecture for visual pathology classification	Gastric pathology detection (e.g., erosions, polyps, cancer)	Showed robust multi-label classification capacity across common gastric pathologies. Aided endoscopists in characterizing mucosal abnormalities with high sensitivity	[106]
Barium X-ray radiography	CNN trained on radiograph features	Gastritis detection from double-contrast GI series	Brought deep learning to a classic imaging modality. Enabled detection of gastritis without the need for endoscopy, useful in areas lacking access to scopes	[107]
Endoscopic images	CNN classifier for esophageal cancer	Esophageal cancer diagnosis using endoscopy	CNN matched the diagnostic accuracy of experienced clinicians. Rapid lesion classification suggested potential use in screening and early detection programs	[108]
Capsule endoscopy images	Deep learning classifier trained with expert input	Small-bowel disease and variant classification	Demonstrated gastroenterologist-level accuracy across a wide range of small-bowel findings. Significantly improved efficiency and reliability in interpreting long capsule videos	[109]

CNN: Convolutional neural network; GI: Gastrointestinal; AI: Artificial intelligence.

Table 3 Applications of convolutional neural networks for lesion segmentation and disease severity assessment in gastrointestinal diseases

Data	Architecture	Application	Key findings	Ref.
Double-balloon endoscopy images	Deep CNN with lesion segmentation and severity scoring	Automated detection and severity grading of Crohn’s ulcers from double-balloon endoscopy	The model accurately segmented ulcers and assessed their severity, enabling objective Crohn’s Disease monitoring. Outperformed traditional scoring methods and showed potential to reduce inter-observer variability	[125]
Capsule endoscopy images	CNN segmentation network trained on angiodysplasias	Automated segmentation of vascular lesions in small bowel images	Provided accurate lesion boundary identification for angiodysplasias, facilitating hemorrhage risk stratification. Supports quicker and more consistent diagnosis compared to manual review	[126]
Colon capsule endoscopy	CNN trained to detect blood and mucosal lesions	Simultaneous detection and segmentation of bleeding and mucosal abnormalities	Enabled precise localization of lesions and bleeding points in colon capsule footage. Improved lesion coverage and reduced diagnostic delay	[127]
Endoscopic images	CNN trained to grade UC severity	Automated assessment of UC severity from colonoscopy	Model achieved expert-level grading accuracy across multiple severity stages. Significantly reduced inter-observer bias, suggesting suitability for clinical trial endpoints	[128]
Colonoscopy images	CNN-based model for pattern recognition	Differentiation of Crohn’s disease vs UC from colonoscopy	The system accurately distinguished UC and Crohn’s disease patterns, providing real-time decision support for disease type classification	[114]
Endoscopic images	Deep learning classifier for depth prediction	Predicting submucosal invasion in gastric neoplasms	Model reliably estimated invasion depth, reducing unnecessary surgical intervention. Useful in pre-treatment risk stratification	[129]
Endoscopic images	CNN for multi-feature prediction	Prediction of early gastric cancer, invasion depth, and differentiation	AI model outperformed experts in cancer invasion depth and differentiation. Enabled non-invasive yet accurate diagnosis during endoscopy	[130]
Capsule endoscopy images	Deep neural network tailored to stricture detection	Detection of Crohn’s-related intestinal strictures	The model improved the detection of strictures, which are often missed in manual review. Accelerates diagnosis and may guide therapeutic decisions	[131]
Confocal laser endomicroscopy images	CNN trained for mucosal healing assessment	Confirmation of mucosal healing in Crohn’s disease	Enabled fine-grained assessment of healing vs inflammation. Provided high-resolution insight for assessing treatment efficacy	[132]
Endoscopic images	AI-assisted classification model	UC disease activity scoring using Mayo classification	Provided consistent and reproducible activity scores. Reduced assessment variability, improving clinical and research utility	[133]
Endoscopic images	CNN vs human graders comparison	Grading UC severity from colonoscopy	CNN showed equal or superior performance to human reviewers in grading UC. Suggested for use in high-throughput settings or trials	[134]
Capsule endoscopy videos	CNN for quantitative feature extraction	Quantitative analysis of celiac disease lesions	Model extracted and quantified villous atrophy and mucosal abnormalities. Offered an objective metric to monitor disease progression	[135]
Conventional endoscopy images	CNN trained on histological labels	Predicting invasion depth of gastric cancer	CNN-assisted depth prediction provided decision support for therapy planning. Could reduce need for unnecessary biopsies	[136]

CNN: Convolutional neural network; UC: Ulcerative colitis; AI: Artificial intelligence.

Table 4 Convolutional neural network-based real-time artificial intelligence support and workflow integration in gastrointestinal endoscopy

Data	Architecture	Application	Key findings	Ref.
Gastroscopy images	Real-time anatomical classification with CNN	Real-time anatomical recognition during gastroscopy procedures	CNN accurately classified anatomical positions in real-time, reducing mislabeling and improving procedural documentation. Enabled smoother workflow integration with minimal latency	[150]
Gastroscopy videos	Frame-wise CNN classification	Automated disease detection during endoscopy video review	The system provided real-time frame classification, improving detection of GI abnormalities in long video sequences and assisting in efficient case triage	[151]
White-light endoscopy	CNN for real-time Helicobacter pylori status	Immediate assessment of Helicobacter pylori infection during endoscopy	Real-time feedback from the CNN allowed on-the-spot therapeutic decision-making and eliminated delays caused by biopsy processing	[113]
Esophagogastroduodenoscopy videos	CNN-based detection system integrated with endoscope	Real-time detection of gastroesophageal varices	The system was validated across multiple centers and significantly reduced miss rates of varices in real-time, enhancing early intervention	[152]
Colonoscopic images	Deep CNN classifier for anatomical site recognition	Automated real-time anatomical classification of colon images	CNN correctly labeled anatomical sites, reducing reliance on user memory and ensuring consistent documentation. Improved novice performance	[153]
Upper GI endoscopy	CNN detection assistant in randomised controlled trial	Detection of gastric neoplasms during routine procedures	A deep learning-based system reduced miss rate of gastric neoplasms significantly in a randomized controlled trial. Validated real-world clinical benefit	[154]
Upper GI anatomy images	Multi-task CNN model	Simultaneous detection of anatomical landmarks and structures	Model performed both classification and segmentation, improving navigation and assisting less experienced users in orientation	[155]
Esophagogastroduodenoscopy images	CNN trained on large anatomical dataset	Real-time classification of esophagogastroduodenoscopy anatomy	CNN classified images with expert-level accuracy, aiding report generation and training in real-time	[141]
GI endoscopy videos	CNN vs global feature comparison	Real-time disease detection efficiency benchmarking	CNNs proved significantly more accurate than traditional global features. Demonstrated readiness for real-time clinical deployment	[156]
GI tract videos	CNN with automated reporting module	End-to-end disease detection and report generation	Automatically generated reports based on real-time CNN classification, reducing documentation time and enhancing standardization	[157]

CNN: Convolutional neural network; GI: Gastrointestinal.