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©The Author(s) 2025.
World J Gastroenterol. May 21, 2025; 31(19): 104897
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.104897
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.104897
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 |
- Citation: Wei W, Zhang XL, Wang HZ, Wang LL, Wen JL, Han X, Liu Q. Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer. World J Gastroenterol 2025; 31(19): 104897
- URL: https://www.wjgnet.com/1007-9327/full/v31/i19/104897.htm
- DOI: https://dx.doi.org/10.3748/wjg.v31.i19.104897