AHFIDAILabs/AcetoWhiteVision

Acetowhite Vision AI is a powerful, smartphone-based application designed to help frontline health workers in low-resource settings perform highly accurate cervical cancer screening. # Acetowhite Vision AI: High-Sensitivity Cervical Cancer Screening A deep learning-powered, smartphone-based tool to empower frontline health workers and revolutionize cervical cancer screening in low-resource settings like Nigeria. This project provides an end-to-end, production-ready MLOps pipeline for training, evaluating, and deploying a high-sensitivity AI model for the early detection of cervical pre-cancerous lesions from VIA (Visual Inspection with Acetic Acid) images. # Table of Contents 1. The Problem 2. Our Solution 3. Live Application 4. Methodology 5. Technology Stack 6. Project Structure 7. Setup and Installation 8. How to Run 9. Future Work 10. 📜 License 11. 🤝 Contributors ## 1. The Problem: The Cervical Cancer Crisis in LMICs Cervical cancer is a preventable disease, yet it claims the lives of over 350,000 women annually, with a staggering 90% of these deaths occurring in Low- and Middle-Income Countries (LMICs). In these regions, the most common screening method remains Visual Inspection with Acetic Acid (VIA), despite WHO calls for more sensitive technologies. The effectiveness of VIA, however, is severely limited by a shortage of trained gynecologists and its subjective nature, which leads to high inter-observer variability and poor accuracy (with sensitivity as low as 36.6%). This critical gap between the need for mass screening and the availability of specialist care results in countless preventable deaths. ## 2. Our Solution: Acetowhite Vision AI Acetowhite Vision AI is designed to bridge this gap. It's a powerful yet simple tool that puts specialist-level accuracy into the hands of frontline health workers. ### Key Features: * Predicts and identifies potential cervical pre-cancerous lesions from standard VIA images. * Integrates a high-sensitivity deep learning model with clinical explainability (Grad-CAM). * Ensures patient data privacy and is designed for offline, on-device processing. * Outputs: A clear VIA Positive/Negative predic …