Wavelet Enhanced Deformable Convolutional Network for Breast Cancer Classification in High Resolution Histopathology Images
Photo Credit: Feepik
An AI-powered breast cancer diagnostic tool is helping close the specialist gap in low-resource settings delivering expert-level accuracy where it's needed most.
In many African countries, access to pathologists and advanced diagnostic infrastructure remains critically limited, meaning thousands of women face delayed or missed breast cancer diagnoses. Late detection dramatically reduces survival rates, yet the shortage of specialists and high-cost equipment makes timely, accurate diagnosis out of reach for most.
Developed and evaluated on the widely used BreaKHis dataset, this AI-powered tool analyses histopathology images to detect breast cancer with 96.47% accuracy at the image level and 96.55% at the patient level with especially strong performance on high-magnification images where fine tissue details are most critical. Crucially, its low computational requirements mean it can run in hospitals and diagnostic centres without high-end hardware or specialist personnel, making it genuinely deployable in under-resourced settings.
The tool offers a scalable, responsible AI pathway to earlier, more accurate breast cancer diagnosis across the continent supporting healthcare providers in delivering better outcomes, and giving patients a fighting chance at survival.