Artificial intelligence-assisted endoscopy in the detection of early gastrointestinal cancer: Progress, challenges, and future directions

Table 1 Explanation of deep convolutional neural network architectures

Architecture name	Core innovation/structural features	Relevance in medical field
VGGNet	Adopts a concise structure of “stacked small convolutional kernels (3 × 3) + pooling layers”, enhancing feature extraction capability by increasing network depth	A classic model for basic feature extraction in medical images, suitable for preliminary lesion detection and medical image classification (e.g., X-ray disease screening), laying the foundation for subsequent architectures in medical AI
ResNet	Introduces “residual connections” (cross-layer feature transmission) to solve the gradient vanishing problem in deep network training, enabling the construction of ultra-deep networks	Significantly improves feature extraction accuracy for complex medical images, applicable to pathological section analysis and 3D medical image segmentation (e.g., tumor boundary extraction), serving as a core architecture for disease diagnosis models
DenseNet	Employs “dense connections” (direct feature sharing across all layers) to enhance feature propagation efficiency and reduce parameter redundancy	Excels in fine-grained analysis of medical images, such as micro-lesion recognition and multi-modal medical image fusion (e.g., combining CT and MRI images), demonstrating distinct advantages in precision medical diagnosis

VGGNet: Visual geometry group network; ResNet: Residual network; DenseNet: Dense convolutional network; AI: Artificial intelligence; 3D: Three dimensional; CT: Computed tomography; MRI: Magnetic resonance imaging.