Erine Cabrelle Toukap Wankap

PhD Overview

This project focuses on developing and evaluating supervised neural networks and a numerical tensor method (NTM) for malaria prediction using thick blood smear microscopy images. The goal is to create a fast, efficient, and cost-effective model to accurately identify the Plasmodium species responsible for malaria. Integrating this model into diagnostic machines would enable rapid diagnosis without the need for specialized expertise or extensive resources. This approach is especially valuable for developing countries, where medical infrastructure and trained professionals may be scarce, ultimately improving malaria detection and treatment at both the image and patient levels.