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.