Image Classification on Urinary stones dataset (5-fold cross-validation)
94.98AccuracyLEPD-Net
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| LEPD-NetInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 94.98 | 82.42 | 78.58 | 76.9 | |
| ARLNetInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 93.48 | 76.17 | 71.38 | 68.98 | |
| MobileNetV3Input resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 93.28 | 78.34 | 70.97 | 67.73 | |
| SMPConv-TInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 93.21 | 74.67 | 69.85 | 68.16 | |
| ResNet18Input resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 92.81 | 76.51 | 67.79 | 65.2 | |
| ConformerInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 92.58 | 72.95 | 67.43 | 64.98 | |
| SwinTransformerInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 91.6 | 65.66 | 57.08 | 56.88 | |
| RepLKNet-BInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 90.88 | 65.89 | 59.84 | 57.68 | |
| MAEInput resolution=224x224, Evaluation protocol=5-fold cross-validation2024.06 | 88.66 | 60.42 | 49.52 | 50.44 |