---
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.