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.

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

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