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Vision Transformer for Retinal Disease Detection
Multi-label Classification using Deep Learning
🏥 Medical AI
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👁️ Retinal Images
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🤖 Vision Transformer
Problem & Solution
🎯 Problem
- Early detection of retinal diseases
- Multiple diseases per image
- Limited specialist availability
- Need for automated screening
💡 Solution
- Vision Transformer architecture
- Multi-label classification
- Advanced data augmentation
- Real-time disease prediction
Model Architecture
Input
224×224 Retinal Images
→
ViT Backbone
Pretrained
google/vit-base-patch16-224
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Classifier
768 → 512 → 256 → 10
LayerNorm + Dropout
Key Components:
• Patch-based attention mechanism
• Multi-head self-attention layers
• Position embeddings for spatial understanding
• Custom classification head for 10 diseases
Target Diseases (10 Classes)
🩸 Diabetic Retinopathy
👁️ Glaucoma
🔍 Macular Degeneration
☁️ Cataracts
💉 Hypertensive Retinopathy
👓 Myopic Retinopathy
🔴 Branch Retinal Vein Occlusion
⭕ Central Retinal Vein Occlusion
📉 Retinal Detachment
✅ Normal/Healthy
Multi-label Classification: One image can have multiple diseases simultaneously
Implementation Highlights
🔧 Training Features
- Advanced data augmentation (Albumentations)
- Differential learning rates
- Cosine annealing scheduler
- Gradient clipping & regularization
📊 Inference Features
- Test-time augmentation (TTA)
- Confidence scoring
- Batch prediction support
- Probability thresholding
Loss Function: BCEWithLogitsLoss for multi-label classification
Optimizer: AdamW with weight decay
Metrics: Accuracy, Classification Report, Confusion Matrix
Impact & Applications
🎯 Clinical Impact
- Early disease detection
- Reduced diagnostic time
- Screening automation
- Healthcare accessibility
🚀 Technical Advantages
- Transformer attention mechanisms
- Transfer learning benefits
- Robust augmentation pipeline
- Production-ready inference
🔮 Future Directions
Integration with medical imaging systems, real-time mobile deployment, and continuous learning from new data