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.
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
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