---
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
---

![Add a heading.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/AubiFwkdFgFgN6KfHIVhb.png)

# **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
 ```

![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/aoLW8h2vfmEPH60676rnb.png)


---

## **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**