Early stage ML based non invasive breast cancer screening

Abstract

Early breast cancer detection in low- and middle-income countries is limited by high screening costs, lack of infrastructure, and dependence on specialized facilities. This project presents a portable, low-cost, non-invasive AI-assisted breast cancer screening system using infrared thermography and machine learning, designed for deployment in resource-constrained settings. The system captures multi-view thermal images and analyzes temperature asymmetry and abnormal heat patterns using a convolutional neural network deployed on an embedded edge-computing platform. A structured engineering approach was followed, including evaluation of multiple design alternatives, optimization, sustainability, economic analysis, ethical compliance, and project management. The system provides an output, Benign or Malignant, to support clinical decision-making without replacing diagnostic procedures. The results demonstrate technical feasibility, affordability, and sustainability, establishing a strong foundation for IRB-guided clinical validation and scalable community-level screening.