| title: WoundNetB7 DFU Analysis | |
| emoji: 🩺 | |
| colorFrom: blue | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: "5.29.0" | |
| python_version: "3.11" | |
| app_file: app.py | |
| pinned: false | |
| # WoundNetB7 — DFU Analysis Pipeline | |
| Complete pipeline for Diabetic Foot Ulcer analysis: | |
| 1. **Binary segmentation** (ulcer detection, Dice: 0.927) | |
| 2. **Multiclass segmentation** (background / foot / perilesion / ulcer) | |
| 3. **Fitzpatrick/ITA** skin type estimation (86.9% accuracy) | |
| 4. **PWAT scores** with Fitzpatrick debiasing (46.6% group gap reduction) | |
| ## Features | |
| - **Guided camera capture** with foot silhouette overlay for healthcare workers | |
| - **PDF clinical report** downloadable with all results | |
| - **JSON output** for system integration | |
| ## Model | |
| EfficientNet-B7 + ASPP + CBAM + CoordAttention + TAM (Topological Attention Module) | |
| Trained with Combo Loss + Small Object Focal Loss. 6-fold TTA at inference. | |