A Machine and Deep Learning-Based Approach for the Detection of Plasmodium vivax: Towards Improved Malaria Diagnosis

Plasmodium vivax (P. vivax) remains a major contributor to global malaria morbidity and mortality, particularly outside sub-Saharan Africa. Its unique biological traits including dormant liver stages, low parasitemia, and early gametocyte development pose significant diagnostic and treatment challenges. Despite advancements in malaria control, P. vivax continues to evade elimination efforts. This review examines the current landscape of P. vivax detection, focusing on recent developments in machine learning (ML) and deep learning (DL) techniques applied to thin blood smear image analysis. A systematic selection of peer-reviewed studies from 2010 to 2024, alongside clinical trial data, was analyzed to evaluate the effectiveness, challenges, and future prospects of AI-based diagnostic models. Notably, lightweight convolutional neural networks (CNNs) like MobileNet and detection frameworks such as YOLO have shown promising results in terms of accuracy and computational efficiency. However, limitations related to generalizability, data variability, and model interpretability remain. This review also outlines biological complexities, drug-resistance issues, and the global and Indian epidemiological context of P. vivax. By synthesizing technical, clinical, and biological perspectives, this work aims to guide future research toward more effective, accessible, and scalable AI-assisted malaria diagnostic tools.