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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- f1 |
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pipeline_tag: image-classification |
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library_name: keras |
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tags: |
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- biology |
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- medical |
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--- |
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# 🦴 Knee Osteoarthritis X-ray Classifier |
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This model classifies grayscale knee X-ray images into 5 severity classes: |
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- **Normal** |
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- **Doubtful** |
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- **Mild** |
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- **Moderate** |
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- **Severe** |
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## 📊 Model Details |
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- Model: CNN built with Keras |
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- Input shape: (162, 300, 1) |
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- Preprocessing: Grayscale conversion, resizing, internal normalization (`Rescaling(1./255)`) |
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- Data Augmentation: Flip, rotation, zoom |
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- Output: Softmax probability over 5 classes |
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## 🧾 Dataset Description |
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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: |
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1. Normal |
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2. Doubtful |
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3. Mild |
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4. Moderate |
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5. Severe |
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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. |
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Link to dataset: [Digital Knee X-ray Images (Kaggle)](https://www.kaggle.com/datasets/orvile/digital-knee-x-ray-images/data) |
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## 📈 Training Summary |
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- Epochs: 100 with early stopping (83) |
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- Optimizer: Adam |
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- Loss: Sparse Categorical Crossentropy |
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- Metric: F1 Score |
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## 🚀 Usage |
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```python |
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from keras.models import load_model |
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model = load_model("knee_oa_classifier.keras") |
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# Preprocess and predict (image should be (162, 300, 1) when using a url to an image |
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response = requests.get(url) |
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img = Image.open(BytesIO(response.content)) |
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img = img.convert('L').resize((162, 300)) |
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display(img) |
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img_array = np.array(img) |
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img_array = img_array.reshape((1, 162, 300, 1)) # Add batch and channel dimensions |
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pred_probs = model.predict(img_array) |
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pred_class_index = np.argmax(pred_probs) |
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pred_class_label = train_ds.class_names[pred_class_index] |
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for pred_prob in pred_probs: |
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for i, class_name in enumerate(train_ds.class_names): |
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display(f'{class_name} -> {pred_prob[i]*100}') |
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display('') |
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``` |
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## 🖼 Example Prediction |
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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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") |
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Class probabilities: |
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- 0Normal -> 0.0 |
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- 1Doubtful -> 0.0 |
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- 2Mild -> 100.0 |
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- 3Moderate -> 0.0 |
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- 4Severe -> 0.0 |
