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

Figure 6 Variation of loss function and validation accuracy during model training. A-D: The changes in loss function values during training for the multi-layer perceptron, residual network, transformer, and Wave-Vision Transformer (Wave-ViT) models, respectively, on the training and validation sets. This visualization illustrates the convergence behavior, generalization ability, and risk of overfitting for each model. By comparing loss function curves, we can assess training stability and efficiency. In this study, if Wave-ViT exhibits faster convergence and lower validation loss, this directly supports its superiority in esophageal cancer diagnosis. This would indicate that Wave-ViT can more effectively learn complex features in medical images while avoiding overfitting, providing strong evidence for its practical application. MLP: Multi-layer perceptron; ResNet: Residual network; Wave-ViT: Wave-Vision Transformer.