Ahmed-Selem/Shifaa-Diabetic-Retinopathy-EfficientNetB0

Diabetic Retinopathy Model

Model Information:

• Architecture: EfficientNet-B0
• Task: Multi-class classification (5 severity levels)
• Dataset: Diabetic Retinopathy Dataset
• Input Size: 224×224 RGB images

Classes:

1. No_DR (No Diabetic Retinopathy)
2. Mild
3. Moderate
4. Severe
5. Proliferate_DR

Performance Metrics:

• Accuracy: 98.55%
• Precision: 0.9861
• Recall: 0.9855
• F1-Score: 0.9856

Usage:

from shifaa.vision import VisionModelFactory

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

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

Confusion Matrix:

Preprocessing:

• Resize to 224×224
• Random horizontal flip (training)
• Random rotation ±10° (training)
• Normalize: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]

Training Details:

• Loss Function: CrossEntropyLoss
• Optimizer: Adam (lr=0.001)
• Batch Size: 64
• Epochs: 30
• Device: CUDA/CPU

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