---
license: apache-2.0
tags:
  - yolov8
  - image-classification
  - medical
  - wound-detection
  - smart-triage
datasets:
  - yasinpratomo/wound-dataset
  - ibrahimfateen/wound-classification
  - shubhambaid/skin-burn-dataset
metrics:
  - accuracy
pipeline_tag: image-classification
---

# Smart Triage Ambulance — Wound Classification Model

YOLOv8n-cls fine-tuned on multi-source wound imagery for real-time surface injury classification in ambulance triage scenarios.

## Classes

| Class | Description |
|-------|-------------|
| `wound` | Open injuries — lacerations, cuts, abrasions, stab wounds |
| `burn` | Thermal damage — 1st, 2nd, 3rd degree burns |
| `contusion` | Closed injuries — bruises, swelling without skin break |

## Training Details

- **Base model:** YOLOv8n-cls (ImageNet pretrained)
- **Dataset:** 3 Kaggle sources, quality-audited (blur, resolution, dedup), class-balanced via ambulance-specific augmentation
- **Optimizer:** AdamW, lr=0.001, freeze=10 layers, dropout=0.3
- **Image size:** 224×224

## Usage

```python
from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("wound_image.jpg")
print(results[0].names[results[0].probs.top1])
```

## Intended Use

Prototype component for the Smart Triage Ambulance system. **Not for clinical diagnosis.** Designed for real-time paramedic decision support during patient transit.