---
license: mit
base_model:
- google/efficientnet-b0
pipeline_tag: image-classification
tags:
- biology
- medical
---
# Eye Disease Model

**Model Information:**
- **Architecture:** EfficientNet-B0
- **Task:** Multi-class classification (4 diseases)
- **Dataset:** [Eye Disease Dataset](https://www.kaggle.com/datasets/gunavenkatdoddi/eye-diseases-classification)
- **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:**
```python
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:**

![Confusion Matrix](./ED_CM.png)


**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)

---