Ahmed-Selem/Shifaa-Eye-Disease-EfficientNetB0

Eye Disease Model

Model Information:

• Architecture: EfficientNet-B0
• Task: Multi-class classification (4 diseases)
• Dataset: Eye Disease Dataset
• Input Size: 224×224 RGB images

Classes:

1. Cataract
2. Diabetic Retinopathy
3. Glaucoma
4. Normal

Performance Metrics:

• Accuracy: 95%
• Precision: 0.95
• Recall: 0.9555
• F1-Score: 0.95

Usage:

from shifaa.vision import VisionModelFactory

model = VisionModelFactory.create_model(
    model_type="classification",
    model_name="Eye_Disease"
)

result = model.run("eye_image.jpg", show_image=True)
print(f"Disease: {result['predicted_class']}")
print(f"Confidence: {result['confidence']:.2f}%")

Confusion Matrix:

Preprocessing:

• Resize to 224×224
• Convert to tensor
• ImageNet normalization

Training Details:

• Loss Function: CrossEntropyLoss
• Optimizer: Adam (lr=0.0001)
• Scheduler: ReduceLROnPlateau (factor=0.5, patience=3)

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