Medical Image Classification for Multi-Disease Diagnosis
Banking & Finance
Data capture & ML consulting services to map out the way to a custom
Built a deep learning pipeline to classify diseases from medical images, achieving 96% accuracy in pneumonia, 94% in brain tumors, and 91% in skin cancer. Used ensemble models (ResNet-50, Dense Net-121, custom CNNs), ad- vanced preprocessing, and GRAD-CAM for explainability. Delivered a web-based diagnostic tool with confidence scoring.
Conclusion
This project demonstrates our capability to build accurate and interpretable deep learning solutions for medical diagnostics. By combining ensemble modeling, explainability techniques, and a user-friendly web interface, we delivered a powerful tool to support faster, more confident clinical decision-making.