README.md

metadata

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.

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