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+knee_oa_classifier.keras filter=lfs diff=lfs merge=lfs -text

README.md

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----
-license: mit
----
+# 🦴 Knee Osteoarthritis X-ray Classifier
+
+This model classifies grayscale knee X-ray images into 5 severity classes:
+
+- **Normal**
+- **Doubtful**
+- **Mild**
+- **Moderate**
+- **Severe**
+
+## 📊 Model Details
+
+- Model: CNN built with Keras
+- Input shape: (162, 300, 1)
+- Preprocessing: Grayscale conversion, resizing, internal normalization (`Rescaling(1./255)`)
+- Data Augmentation: Flip, rotation, zoom
+- Output: Softmax probability over 5 classes
+
+## 🧾 Dataset Description
+This model was trained on the Digital Knee X-ray Images dataset available on Kaggle. The dataset contains labeled grayscale knee X-ray images categorized into:
+
+1. Normal
+2. Doubtful
+3. Mild
+4. Moderate
+5. Severe
+
+These categories represent the Kellgren and Lawrence grading system for osteoarthritis severity. The images are organized into corresponding folders and include both healthy and osteoarthritic knee conditions.
+
+Link to dataset: [Digital Knee X-ray Images (Kaggle)](https://www.kaggle.com/datasets/orvile/digital-knee-x-ray-images/data)
+
+## 📈 Training Summary
+
+- Epochs: 100 with early stopping (83)
+- Optimizer: Adam
+- Loss: Sparse Categorical Crossentropy
+- Metric: F1 Score
+
+## 🚀 Usage
+
+```python
+from keras.models import load_model
+model = load_model("knee_oa_classifier.keras")
+
+# Preprocess and predict (image should be (162, 300, 1) when using a url to an image
+response = requests.get(url)
+img = Image.open(BytesIO(response.content))
+img = img.convert('L').resize((162, 300))
+display(img)
+img_array = np.array(img) 
+img_array = img_array.reshape((1, 162, 300, 1))  # Add batch and channel dimensions
+
+pred_probs = model.predict(img_array)
+pred_class_index = np.argmax(pred_probs)
+pred_class_label = train_ds.class_names[pred_class_index]
+
+for pred_prob in pred_probs:
+    for i, class_name in enumerate(train_ds.class_names):
+        display(f'{class_name} -> {pred_prob[i]*100}')
+    display('')
+```
+
+## 🖼 Example Prediction
+
+Image [link]("https://storage.googleapis.com/kagglesdsdata/datasets/5697473/9389485/OS%20Collected%20Data/Osteopenia/Osteopenia%2010.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20250513%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250513T112322Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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")
+
+Class probabilities:
+- 0Normal -> 0.0
+- 1Doubtful -> 0.0
+- 2Mild -> 100.0
+- 3Moderate -> 0.0
+- 4Severe -> 0.0

knee_oa_classifier.keras

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