Improving in-the-field performance of automated malaria diagnosis

Abstract

Malaria remains a leading cause of mortality, particularly in regions with limited access to healthcare. Despite the availability of diagnostic methods such as microscopy, Rapid Diagnostic Tests (RDT), and Polymerase Chain Reaction (PCR), these methods face challenges in accuracy and practicality, especially in field conditions. This thesis investigates the use of automated, Computer-aided diagnostics (CAD) to improve in-the-field malaria diagnosis. While CAD shows promise, significant challenges remain in dealing with image imperfections and data variability. The study focuses on the use of blob detectors and Zernike decomposition to improve the precision of the CAD system. Additionally, data quality is investigated to evaluate the reliability of malaria diagnostic pipeline. The results suggest that Zernike decomposition is effective in identifying shapes most likely to correspond to parasites, enhancing the blob detector’s precision. Furthermore, the use of data stratification is shown to reduce the variance between models during training, which indicates that it can generalise better to unseen data. This work contributes to the ongoing effort to develop a robust, field-deploy-able malaria diagnostic tool by incorporating prior knowledge of the malaria parasite its shape to improve precision and ensuring reliability by evaluating data quality.