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

Figure 3 Convolution and pooling diagram. A: Convolution operation with a 3 × 3 kernel resulting in a 4 × 4 output; B: Max-pooling operation using a 2 × 2 filter and stride 2 × 2, reducing feature map dimensions to 2 × 2. These operations illustrate how convolutional neural networks extract features and reduce spatial resolution while preserving critical information.