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---
title: bfd-rg
sdk: gradio
emoji: 🏃
colorFrom: green
colorTo: yellow
pinned: false
---
# **Bone-Fracture-Detection**
> **Bone-Fracture-Detection** is a binary image classification model based on `google/siglip2-base-patch16-224`, trained to detect **fractures in bone X-ray images**. It is designed for use in **medical diagnostics**, **clinical triage**, and **radiology assistance systems**.
```py
Classification Report:
precision recall f1-score support
Fractured 0.8633 0.7893 0.8246 4480
Not Fractured 0.8020 0.8722 0.8356 4383
accuracy 0.8303 8863
macro avg 0.8326 0.8308 0.8301 8863
weighted avg 0.8330 0.8303 0.8301 8863
```
---
## **Label Classes**
The model distinguishes between the following bone conditions:
```
0: Fractured
1: Not Fractured
```
---
## **Installation**
```bash
pip install transformers torch pillow gradio
```
---
## **Example Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
# Load model and processor from the Hugging Face Hub
model_name = "prithivMLmods/Bone-Fracture-Detection"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def detect_fracture(image):
"""
Takes a NumPy image array, processes it, and returns the model's prediction.
"""
# Convert NumPy array to a PIL Image
image = Image.fromarray(image).convert("RGB")
# Process the image and prepare it as input for the model
inputs = processor(images=image, return_tensors="pt")
# Perform inference without calculating gradients
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Apply softmax to get probabilities and convert to a list
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
# Create a dictionary of labels and their corresponding probabilities
# This now correctly uses the labels from the model's configuration
prediction = {model.config.id2label[i]: round(probs[i], 3) for i in range(len(probs))}
return prediction
# Create the Gradio Interface
iface = gr.Interface(
fn=detect_fracture,
inputs=gr.Image(type="numpy", label="Upload Bone X-ray"),
outputs=gr.Label(num_top_classes=2, label="Detection Result"),
title="🔬 Bone Fracture Detection",
description="Upload a bone X-ray image to detect if there is a fracture. The model will return the probability for 'Fractured' and 'Not Fractured'.",
examples=[
["fractured_example.png"],
["not_fractured_example.png"]
] # Note: You would need to have these image files in the same directory for the examples to work.
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
---
## **Applications**
* **Orthopedic Diagnostic Support**
* **Emergency Room Triage**
* **Automated Radiology Review**
* **Clinical Research in Bone Health** |