Review Paper:
Revolutionizing
Malaria Diagnosis: A computer aided approach for the Detection of Plasmodium vivax
using Machine and Deep Learning techniques
Prathap V.M., Qidwai T. and Yadav S.
Res. J. Biotech.; Vol. 20(4); 215-225;
doi: https://doi.org/10.25303/204rjbt2150225; (2025)
Abstract
Machine learning (ML) and Deep learning (DL) methods are widely applied in the medical
field because of their high diagnostic accuracy. Malaria is one such illness that
is brought on by Plasmodium vivax (P. vivax) and spread by the female Anopheles
mosquito. P. vivax has a substantial negative impact on health around the globe.
Although research has been done on diagnosis and detection through various techniques,
there are still multiple gaps in P. vivax diagnosis. This review focuses on the
number of deaths caused by the P. vivax species worldwide, as well as the most recent
developments in ML and DL approaches for diagnosing malaria. In order to successfully
diagnose malaria, an ML approach must overcome a number of obstacles.
Currently, a number of studies are being conducted to examine the interpretability
of models using ML and DL techniques for P. vivax identification. It also examines
the potential for P. vivax detection in the future. Through the use of modern, widely-used
methodologies like ML and DL, this study will contribute to our knowledge of the
P. vivax malaria situation as on today and will help us to uncover its background
including its biology, global endemicity, methods of diagnosis through ML and DL
techniques and the challenges involved.
