---
title: Chronic Wound Classifier
emoji: 🩺
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.49.1
python_version: "3.11"
app_file: app.py
pinned: false
license: mit
---

# Chronic Wound Classifier — 4-class AZH demo

Demo classifier for chronic wound photographs: predicts one of four wound types
(diabetic ulcer, pressure ulcer, surgical wound, venous ulcer) from an uploaded image.

**Not a medical device. Not for clinical use.** Research demonstration only.

## Headline metric
Top-1 accuracy on the held-out AZH Test set (n=184): **0.8152**
(`cv_baseline_fold5_best.pt` — the highest single-fold checkpoint from
patient-grouped 10-fold cross-validation).

The 10-fold soft-vote ensemble of the same recipe scores 0.7989 on the same
set; the single-checkpoint variant is shipped here for inference latency
and footprint reasons.

## Architecture
EfficientNet-B0 (ImageNet-pretrained), two-phase fine-tune (head-only 5 epochs
at lr=1e-3, then full network 15 epochs at lr=1e-4). Patient-grouped CV
splits ensure the same patient's images never appear in both train and val.

## Limitations
- **Pressure-class accuracy is ~0.41** — interpret pressure-class predictions with care.
- No fairness audit across skin tones (known gap).
- English-only UI; no mobile or offline build.
- Not validated on real patient cohorts outside AZH.

## Source code & training pipeline
The training, evaluation, and methodology code live in the project repo:
[github.com — wound-classification](#) (full link to be added by user)

## Citation
Anisuzzaman et al. 2022. *Multi-modal wound classification using wound image
and location by deep neural network.* Sci. Rep. 12:20057.