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---
license: apache-2.0
datasets:
- Hemg/bone-fracture-detection
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Bone
- Fracture
- Detection
- SigLIP2
- medical
- biology
---
# **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, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Bone-Fracture-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# ID to label mapping
id2label = {
"0": "Fractured",
"1": "Not Fractured"
}
def detect_fracture(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=detect_fracture,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Fracture Detection"),
title="Bone-Fracture-Detection",
description="Upload a bone X-ray image to detect if there is a fracture."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Applications**
* **Orthopedic Diagnostic Support**
* **Emergency Room Triage**
* **Automated Radiology Review**
* **Clinical Research in Bone Health** |