Research Article
Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System
Table 6
Classification report of the best combination for each transfer learning model.
| Model | | Precision | Recall | -score | Model | | Precision | Recall | -score |
| ViT (Google) | Benign | 0.96950 | 0.95300 | 0.96120 | ViT (VAN) | Benign | 0.93620 | 0.91000 | 0.92290 | Malignant | 0.95380 | 0.97000 | 0.96180 | Malignant | 0.91250 | 0.93800 | 0.92500 | Accuracy | 0.96150 | Accuracy | 0.92400 | Macro- | 0.96150 | Macro- | 0.92400 | Weighted- | 0.96150 | Weighted- | 0.92400 |
| ViT (Mae) | Benign | 0.94230 | 0.89800 | 0.91960 | ViT (BEiT) | Benign | 0.91430 | 0.80000 | 0.85330 | Malignant | 0.90260 | 0.94500 | 0.92330 | Malignant | 0.82220 | 0.92500 | 0.87060 | Accuracy | 0.92150 | Accuracy | 0.86250 | Macro- | 0.92150 | Macro- | 0.86200 | Weighted- | 0.92150 | Weighted- | 0.86200 |
| ViT (ResNet50) | Benign | 0.81470 | 0.80000 | 0.80730 | ViT (DiT) | Benign | 0.83160 | 0.80000 | 0.81550 | Malignant | 0.80350 | 0.81800 | 0.81070 | Malignant | 0.80730 | 0.83800 | 0.82240 | Accuracy | 0.80900 | Accuracy | 0.81900 | Macro- | 0.80900 | Macro- | 0.81890 | Weighted- | 0.80900 | Weighted- | 0.81890 |
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