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

Table 1 Distribution of samples across the dataset

	Esophagitis A	Esophagitis B-D	Barretts-short-segment	Barretts
Tarin set	320	208	42	32
Test set	42	26	6	5
Val set	41	26	5	4
Total	403	260	53	41

Table 2 Performance metrics of four deep learning models

Model name	Accuracy (%)	Input size (MB)	Params (MB)	Madd (G)	Flops (G)
MLP	81.17	3.15	1.10	1.57	0.95
ResNet	85.44	3.15	45.20	71.21	35.63
Transfomer	87.65	3.15	1.45	2.17	1.57
Wave-ViT	88.97	3.15	1.29	1.04	1.12

MLP: Multi-layer perceptron; ResNet: Residual network; Wave-ViT: Wave-Vision Transformer.