Deep learning model for early detection of diseases from medical imaging
A major hospital network needed to scale their radiology department without hiring more specialists. We developed an AI-powered diagnostic system that analyzes X-rays, CT scans, and MRIs to detect abnormalities with 98.5% accuracy, reducing diagnosis time by 75% while maintaining the highest standards of medical care.
Hospital needed to reduce diagnosis time and improve accuracy for radiology scans while handling 500+ daily scans.
Developed a custom CNN-based model trained on 100K+ labeled medical images with 98.5% accuracy. Integrated with existing PACS system for seamless workflow.
Multi-model ensemble architecture with DICOM integration, real-time processing pipeline, and human-in-the-loop review system.
Collected and labeled 100K medical images, ensured HIPAA compliance, created train/val/test splits
Trained multiple architectures, performed hyperparameter tuning, achieved 98.5% validation accuracy
Integrated with PACS, built API and dashboard, conducted clinical trials with radiologists
Deployed to production, trained staff, established monitoring and feedback loops
Let's discuss how we can build a custom AI solution tailored to your needs.