Spaces:
Sleeping
Sleeping
| title: bfd-rg | |
| sdk: gradio | |
| emoji: 🏃 | |
| colorFrom: green | |
| colorTo: yellow | |
| pinned: false | |
|  | |
| # **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, AutoModelForImageClassification | |
| from PIL import Image | |
| import torch | |
| # Load model and processor from the Hugging Face Hub | |
| model_name = "prithivMLmods/Bone-Fracture-Detection" | |
| model = AutoModelForImageClassification.from_pretrained(model_name) | |
| processor = AutoImageProcessor.from_pretrained(model_name) | |
| def detect_fracture(image): | |
| """ | |
| Takes a NumPy image array, processes it, and returns the model's prediction. | |
| """ | |
| # Convert NumPy array to a PIL Image | |
| image = Image.fromarray(image).convert("RGB") | |
| # Process the image and prepare it as input for the model | |
| inputs = processor(images=image, return_tensors="pt") | |
| # Perform inference without calculating gradients | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| # Apply softmax to get probabilities and convert to a list | |
| probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() | |
| # Create a dictionary of labels and their corresponding probabilities | |
| # This now correctly uses the labels from the model's configuration | |
| prediction = {model.config.id2label[i]: round(probs[i], 3) for i in range(len(probs))} | |
| return prediction | |
| # Create the Gradio Interface | |
| iface = gr.Interface( | |
| fn=detect_fracture, | |
| inputs=gr.Image(type="numpy", label="Upload Bone X-ray"), | |
| outputs=gr.Label(num_top_classes=2, label="Detection Result"), | |
| title="🔬 Bone Fracture Detection", | |
| description="Upload a bone X-ray image to detect if there is a fracture. The model will return the probability for 'Fractured' and 'Not Fractured'.", | |
| examples=[ | |
| ["fractured_example.png"], | |
| ["not_fractured_example.png"] | |
| ] # Note: You would need to have these image files in the same directory for the examples to work. | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch() | |
| ``` | |
| --- | |
| ## **Applications** | |
| * **Orthopedic Diagnostic Support** | |
| * **Emergency Room Triage** | |
| * **Automated Radiology Review** | |
| * **Clinical Research in Bone Health** |