---
language: en
tags:
- medical-imaging
- computer-vision
- efficientnetv2
- keras
- tensorflow
license: mit
---

# MURA Bone Fracture Detection Model

## Model Description
This is a custom-trained **EfficientNetV2** model designed to detect bone fractures in musculoskeletal radiographs. It was trained using transfer learning on the **MURA (Musculoskeletal Radiographs)** dataset. 

* **Architecture:** EfficientNetV2 (Base) + Custom GlobalAveragePooling & Dense Head
* **Task:** Binary Classification (`fractured` vs. `not_fractured`)
* **Framework:** TensorFlow / Keras
* **Input Resolution:** 224x224 RGB images

## Usage
You can load this model directly using TensorFlow/Keras to run inference on new X-ray images:

```python
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Load the model
model = tf.keras.models.load_model('MURA_EfficientNetV2L.h5')

# Preprocess image
img = image.load_img('path_to_xray.jpg', target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = tf.keras.applications.efficientnet_v2.preprocess_input(img_array)

# Predict
prediction = model.predict(img_array)
print(f"Fracture Probability: {prediction[0][0]:.2%}")