Revolutionizing Malaria Diagnosis: A computer aided approach for the Detection of Plasmodium vivax using Machine and Deep Learning techniques

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.