Medical Image Classification on ISIC 2018 (Accuracy, AUC, Sensitivity, Specificity)
92.94AccuracySwinV2-B + PHG + multi-scale + multi-filtrations
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| SwinV2-B + PHG + multi-scale + multi-filtrationsBackbone=SwinV2-B, PHG=true, multi-scale=true, multi-filtrations=true2025.12 | 92.94 | 99.1 | 85.56 | 98.12 | |
| SENet154 + PHG + multi-scale + multi-filtrationsBackbone=SENet154, PHG=true, multi-scale=true, multi-filtrations=true2025.12 | 92.58 | 98.95 | 82.73 | 97.82 | |
| SwinV2-B + PHG + multi-scaleBackbone=SwinV2-B, PHG=true, multi-scale=true, multi-filtrations=false2025.12 | 91.6 | 98.74 | 83.97 | 97.68 | |
| ResNet152 + PHG + multi-scale + multi-filtrationsBackbone=ResNet152, PHG=true, multi-scale=true, multi-filtrations=true2025.12 | 91.56 | 98.81 | 81.11 | 97.48 | |
| SENet154 + PHG + multi-scaleBackbone=SENet154, PHG=true, multi-scale=true, multi-filtrations=false2025.12 | 91.13 | 98.2 | 81.13 | 97.43 | |
| SwinV2-B + PHGBackbone=SwinV2-B, PHG=true, multi-scale=false, multi-filtrations=false2025.12 | 90.87 | 98.13 | 82.36 | 97.39 | |
| SENet154 + PHGBackbone=SENet154, PHG=true, multi-scale=false, multi-filtrations=false2025.12 | 90.47 | 97.75 | 78.55 | 97.31 | |
| ResNet152 + PHG + multi-scaleBackbone=ResNet152, PHG=true, multi-scale=true, multi-filtrations=false2025.12 | 90.16 | 98.34 | 78.97 | 97.31 | |
| ResNet152 + PHGBackbone=ResNet152, PHG=true, multi-scale=false, multi-filtrations=false2025.12 | 89.63 | 97.47 | 76.62 | 97.07 | |
| SwinV2-BBackbone=SwinV2-B, PHG=false, multi-scale=false, multi-filtrations=false2025.12 | 89.03 | 97.77 | 79.63 | 97.03 | |
| SENet154Backbone=SENet154, PHG=false, multi-scale=false, multi-filtrations=false2025.12 | 88.03 | 97.03 | 75.94 | 96.83 | |
| ResNet152Backbone=ResNet152, PHG=false, multi-scale=false, multi-filtrations=false2025.12 | 87.68 | 96.7 | 70.82 | 96.6 |