ResNet-18 Cataract Detection System
| Property | Value |
|---|---|
| Architecture | ResNet-18 |
| Task | Binary Classification (Normal/Cataract) |
| Input Size | 224x224 pixels |
| Accuracy | 97.52% |
| Framework | PyTorch |
| Author | AventIQ-AI |
What is resnet18-cataract-detection-system?
The resnet18-cataract-detection-system is a specialized deep learning model designed for automated cataract detection in medical imaging. Built on the efficient ResNet-18 architecture, this quantized model achieves impressive accuracy while maintaining computational efficiency. It processes standard 224x224 pixel images and classifies them into two categories: normal or cataract.
Implementation Details
The model utilizes a fine-tuned ResNet-18 architecture, optimized through careful training on a comprehensive cataract dataset. The implementation includes standard image preprocessing with normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) and achieves remarkable metrics with 98.31% precision and 96.67% recall.
- Quantized model for efficient deployment
- Binary classification architecture
- Comprehensive preprocessing pipeline
- PyTorch-based implementation
Core Capabilities
- High-accuracy cataract detection (97.52% accuracy)
- Efficient processing of medical images
- Real-time classification capability
- Robust performance metrics (97.48% F1-Score)
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of ResNet-18 architecture with specialized training for cataract detection. Its quantized nature makes it particularly suitable for deployment in resource-constrained environments while maintaining high accuracy.
Q: What are the recommended use cases?
The model is designed for preliminary screening in medical settings, assisting healthcare professionals in cataract detection. However, it should be used as a supportive tool rather than a replacement for professional medical diagnosis.
