COVID-19 X-ray Classification
Deep learning for medical imaging with 92% ROC-AUC and Grad-CAM explainability for clinical decision support
Technology Stack
PyTorchResNet50EfficientNetDenseNetGrad-CAMCLAHEStreamlitAlbumentations
ML Frameworks
Deep Learning
NLP
Deployment
ROI
$1.05M per year
Savings
$700K per radiology department
Accuracy
68.7% overall, 92.1% ROC-AUC
Key Performance Indicators
COVID-19 Sensitivity
67.0%Time Savings
90% (5 min → 30 sec)Throughput Increase
+60% (250 → 400 studies/day)Business Impact Summary
| Return on Investment | $1.05M per year |
| Cost Savings | $700K per radiology department |
| Model Accuracy | 68.7% overall, 92.1% ROC-AUC |
| COVID-19 Sensitivity | 67.0% |
| Time Savings | 90% (5 min → 30 sec) |
| Throughput Increase | +60% (250 → 400 studies/day) |
Overview
State-of-the-art deep learning system classifying chest X-rays into COVID-19, Viral Pneumonia, Lung Opacity, and Normal with 92.1% ROC-AUC. Grad-CAM visualizations provide clinical explainability. Designed for emergency department triage and radiology workflow optimization.
Live Demo
Live Demo
Interactive Streamlit application
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COVID-19 X-ray Classification Demo
Click below to load the interactive demo
This is a live, interactive demo deployed on Streamlit Cloud. You can:
- •Upload your own data for predictions
- •Explore model performance metrics
- •View interactive visualizations
- •Understand model predictions with explainability