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README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- medical
+- gerd
+- vision-transformer
+- tensorflow
+- image-classification
+datasets:
+- custom-gerd-dataset
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: GERD Lightweight ViT
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: GERD Augmented Dataset
+      type: custom
+    metrics:
+    - type: accuracy
+      value: 0.95
+      name: Accuracy
+---
+
+# GERD Lightweight Vision Transformer 🔬
+
+This is a lightweight Vision Transformer (ViT) model trained to classify gastroesophageal images into 4 categories:
+- **Esophagitis**: Inflammation of the esophagus
+- **GERD**: Gastroesophageal Reflux Disease
+- **Normal**: Healthy esophagus
+- **Ulcer**: Esophageal ulceration
+
+## Model Details
+
+### Architecture
+- **Model Type**: Lightweight Vision Transformer (ViT)
+- **Input Size**: 224x224x3 (RGB)
+- **Patch Size**: 8x8
+- **Projection Dimension**: 64
+- **Transformer Layers**: 4
+- **Attention Heads**: 4
+- **MLP Head Units**: [128, 64]
+- **Output Classes**: 4
+
+### Training
+- Trained using 5-Fold Cross-Validation
+- Optimizer: Adam (lr=1e-4)
+- Loss: Categorical Crossentropy with Label Smoothing (0.1)
+- Early Stopping with patience=20
+- Data Augmentation applied
+
+### Performance
+- High accuracy on GERD classification tasks
+- Optimized for medical image analysis
+- Efficient inference with reduced parameters
+
+## Intended Use
+
+This model is designed for **research and educational purposes** in medical image analysis. It should **NOT** be used as the sole diagnostic tool in clinical settings.
+
+### Direct Use
+```python
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model('model.keras')
+
+# Prepare image
+image = Image.open('your_image.jpg').resize((224, 224))
+image_array = np.array(image) / 255.0
+image_array = np.expand_dims(image_array, axis=0)
+
+# Predict
+predictions = model.predict(image_array)
+class_names = ['Esophagitis', 'GERD', 'Normal', 'Ulcer']
+predicted_class = class_names[np.argmax(predictions)]
+confidence = np.max(predictions)
+
+print(f"Predicted: {predicted_class} (Confidence: {confidence:.2%})")
+```
+
+## Limitations and Bias
+
+⚠️ **Important Disclaimers:**
+- This model is trained on a specific dataset and may not generalize to all populations
+- Medical imaging interpretation requires clinical expertise
+- Always consult healthcare professionals for medical decisions
+- The model may have biases based on the training data distribution
+
+## Training Data
+
+The model was trained on an augmented GERD dataset containing gastroesophageal images across 4 categories.
+
+## Citation
+
+If you use this model, please cite appropriately and acknowledge the original dataset sources.
+
+## Contact
+
+For questions or issues, please open an issue in the model repository.
+
+---
+*Developed for medical image classification research*

config.json

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+{
+  "model_type": "vision-transformer",
+  "task": "image-classification",
+  "image_size": 224,
+  "patch_size": 8,
+  "projection_dim": 64,
+  "transformer_layers": 4,
+  "num_heads": 4,
+  "mlp_head_units": [
+    128,
+    64
+  ],
+  "num_classes": 4,
+  "class_names": [
+    "Esophagitis",
+    "GERD",
+    "Normal",
+    "Ulcer"
+  ],
+  "framework": "tensorflow",
+  "preprocessing": {
+    "resize": [
+      224,
+      224
+    ],
+    "normalize": "scale_0_1"
+  }
+}

inference_example.py

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+
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import requests
+from io import BytesIO
+
+# Load model
+model = tf.keras.models.load_model('model.keras')
+
+# Class names
+CLASS_NAMES = ['Esophagitis', 'GERD', 'Normal', 'Ulcer']
+
+def predict_from_url(image_url):
+    response = requests.get(image_url)
+    image = Image.open(BytesIO(response.content))
+    return predict_image(image)
+
+def predict_from_path(image_path):
+    image = Image.open(image_path)
+    return predict_image(image)
+
+def predict_image(image):
+    # Preprocess
+    image = image.convert('RGB')
+    image = image.resize((224, 224))
+    image_array = np.array(image) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+    
+    # Predict
+    predictions = model.predict(image_array)[0]
+    
+    # Format results
+    results = {
+        CLASS_NAMES[i]: float(predictions[i]) 
+        for i in range(len(CLASS_NAMES))
+    }
+    
+    predicted_class = CLASS_NAMES[np.argmax(predictions)]
+    confidence = float(np.max(predictions))
+    
+    return {
+        'predicted_class': predicted_class,
+        'confidence': confidence,
+        'all_predictions': results
+    }
+
+# Example usage
+if __name__ == "__main__":
+    result = predict_from_path('test_image.jpg')
+    print(f"Prediction: {result['predicted_class']}")
+    print(f"Confidence: {result['confidence']:.2%}")
+    print(f"All probabilities: {result['all_predictions']}")

model.keras

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+size 938108