metadata
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
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
pip install torch segmentation-models-pytorch albumentations huggingface_hub
Download and Load Model
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
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:
- 810 training images
- 200 validation images
- Pixel-wise annotations by wound care experts
Citation
@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
- LinkedIn: Ruthvik Bandari
Disclaimer
This model is intended for research purposes only. Not for clinical diagnosis without professional medical oversight.