| --- |
| 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%}") |
