AI-Enabled Early Cancer Detection Platform

Early-stage oncology diagnostics face a critical bottleneck: the manual review of thousands of high-resolution DICOM slices. Radiologists are under increasing pressure, leading to fatigue-induced oversights in identifying anomalies smaller than 3mm. Current manual screening methods exhibit a baseline accuracy of 82.1%, with a significant 12.5% false positive rate that leads to unnecessary invasive biopsies.

We implemented a multi-stage Deep Convolutional Neural Network (DCNN) architecture specifically optimized for pixel-level anomaly detection in volumetric medical data. The system utilizes a custom feature pyramid network (FPN) to maintain spatial awareness across different zoom levels, ensuring that even sub-millimeter nodules are identified with high confidence. Direct DICOM integration with hospital PACS systems eliminates conversion loss.