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README.md

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+---
+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%}")