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

Figure 2 The basic architecture of a convolutional neural network. The architecture includes an input layer for receiving endoscopic image data, multiple convolutional layers for extracting spatial features, pooling layers to downsample and retain salient regions, fully connected layers to integrate high-level representations, and an output layer for classification or regression. Non-linear activations such as rectified linear unit are used between layers to introduce complexity.