Advancements in Deep Learning for Malaria Detection: A Comprehensive Overview

Malaria remains a critical global health issue, with millions of cases reported annually, particularly in resource-limited regions. Timely and accurate diagnosis is vital to ensure effective treatment, reduce complications, and control transmission. Conventional diagnostic methods, including microscopy and Rapid Diagnostic Tests (RDTs), face considerable limitations such as dependency on skilled personnel, limited sensitivity at low parasitemia levels, and cost constraints. In response, deep learning technologies—especially Convolutional Neural Networks (CNNs)—have emerged as promising tools to overcome these barriers by enabling automated diagnostics based on medical imaging, significantly enhancing precision and scalability. This paper presents a comprehensive review of recent advances in deep learning for malaria diagnosis, highlighting the role of publicly available datasets in driving innovation. It analyzes leading architectures—such as ResNet, VGG, and YOLO—based on their classification performance, including accuracy, sensitivity, and computational efficiency. Furthermore, the review discusses novel directions such as mobile-integrated diagnostics and multi-modal data fusion, which can enhance diagnostic accessibility in low-resource settings. Despite notable progress, challenges remain in terms of dataset imbalance, lack of generalizability, and barriers to clinical deployment. The paper concludes by outlining future research directions and emphasizing the need for robust, adaptable models that can support global malaria control and eradication strategies.

@article{Kabore2025,
  title     = {Advancements in Deep Learning for Malaria Detection: A Comprehensive Overview},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Kiswendsida Kisito Kabore and Desire Guel and Flavien Herve Somda},
  doi       = {10.14569/IJACSA.2025.0160608},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160608}
}