Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer

Figure 3 Bottleneck block of the residual network residual module. The bottleneck block in residual network’s residual module is a key architectural component in deep convolutional neural networks and holds significant research value. This design effectively reduces the number of parameters and computational complexity, enabling networks to be deepened to hundreds of layers without encountering vanishing or exploding gradient issues. Consequently, this enhancement significantly improves the performance of deep learning models in image classification and other tasks.