---
license: cc-by-nc-4.0
library_name: pytorch
pipeline_tag: image-segmentation
tags:
  - pytorch
  - medical-imaging
  - image-segmentation
  - semantic-segmentation
  - wound-segmentation
  - diabetic-foot-ulcer
  - dfu
  - unet
  - unet-plus-plus
  - efficientnet
  - deep-learning
  - computer-vision
  - healthcare
  - medical-ai
  - fuseg
  - miccai
datasets:
  - custom
metrics:
  - iou
  - dice
---

# DiaFoot.AI 🦶

**State-of-the-Art Diabetic Foot Ulcer Segmentation**

[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/Ruthvik-Bandari/DiaFoot.AI)
[![License](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](LICENSE)

## Model Description

DiaFoot.AI is a deep learning model for automatic segmentation of diabetic foot ulcers from clinical images. It uses a U-Net++ architecture with an EfficientNet-B4 encoder pretrained on ImageNet.

### Performance

| Metric | DiaFoot.AI | FUSeg 2021 Winner | Improvement |
|--------|-----------|-------------------|-------------|
| **IoU** | **85.58%** | 80.30% | **+5.28%** |
| **Dice** | **92.23%** | 89.23% | **+3.00%** |

### Cross-Validation Results

| Fold | IoU | Dice |
|------|-----|------|
| Fold 1 | 83.22% | 90.84% |
| Fold 2 | 85.81% | 92.36% |
| Fold 3 | 82.16% | 90.20% |
| Fold 4 | 85.00% | 91.89% |
| Fold 5 | 84.44% | 91.56% |
| **Mean** | **84.13% ± 1.30%** | **91.37% ± 0.77%** |

## Usage

### Installation
```bash
pip install torch segmentation-models-pytorch albumentations huggingface_hub
```

### Download and Load Model
```python
from huggingface_hub import hf_hub_download
import torch
import segmentation_models_pytorch as smp

# Download model
model_path = hf_hub_download("RuthvikBandari/DiaFootAI", "best_model.pt")

# Create model architecture
model = smp.UnetPlusPlus(
    encoder_name="efficientnet-b4",
    encoder_weights=None,
    in_channels=3,
    classes=1
)

# Load weights
checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
state_dict = checkpoint["model_state_dict"]
# Remove 'model.' prefix if present
state_dict = {k.replace("model.", "", 1): v for k, v in state_dict.items()}
model.load_state_dict(state_dict)
model.eval()
```

### Inference
```python
import cv2
import numpy as np
import albumentations as A
from albumentations.pytorch import ToTensorV2

# Preprocessing
transform = A.Compose([
    A.LongestMaxSize(max_size=512),
    A.PadIfNeeded(min_height=512, min_width=512, border_mode=cv2.BORDER_CONSTANT),
    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ToTensorV2()
])

# Load and preprocess image
image = cv2.imread("wound_image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
transformed = transform(image=image)
input_tensor = transformed["image"].unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(input_tensor)
    mask = torch.sigmoid(output).squeeze().numpy()
    binary_mask = (mask > 0.5).astype(np.uint8)
```

## Model Architecture

- **Architecture:** U-Net++
- **Encoder:** EfficientNet-B4 (ImageNet pretrained)
- **Input Size:** 512 × 512 × 3
- **Output:** Single channel probability map
- **Parameters:** 20.8M

## Training Details

- **Loss:** Focal Tversky + BCE with warmup
- **Optimizer:** AdamW
- **Learning Rate:** 3e-4 (decoder), 1e-5 (encoder)
- **Batch Size:** 8
- **Epochs:** 100
- **Augmentation:** Heavy (flips, rotations, color jitter, elastic transform)
- **Test Time Augmentation:** 8 transforms

## Dataset

Trained on [FUSeg 2021 Challenge Dataset](https://github.com/uwm-bigdata/wound-segmentation):
- 810 training images
- 200 validation images
- Pixel-wise annotations by wound care experts

## Citation
```bibtex
@software{bandari2025diafootai,
  author = {Bandari, Ruthvik},
  title = {DiaFoot.AI: Deep Learning for Diabetic Foot Ulcer Segmentation},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/Ruthvik-Bandari/DiaFoot.AI},
  note = {Achieves 85.58% IoU, surpassing MICCAI FUSeg 2021 winner by +5.28%}
}
```

## License

**CC BY-NC 4.0** - You must give appropriate credit and may not use for commercial purposes without permission.

## Author

**Ruthvik Bandari**  
Master's in Applied Artificial Intelligence  
Northeastern University

- GitHub: [@Ruthvik-Bandari](https://github.com/Ruthvik-Bandari)
- LinkedIn: [Ruthvik Bandari](https://www.linkedin.com/in/ruthvik-nath-bandari-908b00247/)

## Disclaimer

This model is intended for research purposes only. Not for clinical diagnosis without professional medical oversight.