Machine and deep learning methods in identifying malaria through microscopic blood smear : A systematic review

• Main Authors: Dhevisha, Sukumarran, Khairunnisa, Hasikin, Anis Salwa, Mohd Khairuddin, Romano, Ngui, Wan Yusoff, Wan Sulaiman, Indra, Vythilingam, Paul Cliff Simon, Divis
• Format: Article
• Language: English
• Published: Elsevier B.V. 2024
• Subjects: Q Science (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://ir.unimas.my/id/eprint/44687/3/Machine.pdf; http://ir.unimas.my/id/eprint/44687/; https://www.sciencedirect.com/science/article/abs/pii/S0952197624006870; https://doi.org/10.1016/j.engappai.2024.108529

Despite the persistency of World Health Organization to eliminate malaria since 1987, malaria disease continues to pose a significant threat to global health. As the severity of malaria persists over the years, there is a critical need for an automated diagnosis system for more efficient diagnosis and effective treatment administration. To mitigate the increase in mosquito-borne diseases, there has been a heightened interest in the application of Artificial intelligence (AI), specifically deep learning. With the assistance of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework, an extensive review of current state of automated malaria diagnosis systems utilizing machine learning and deep learning approaches was performed across eight scientific databases, with 50 articles shortlisted from the years 2015–2023. Besides, identifying the research gaps, we synthesise the existing literature, analyse the outcomes, and explore the critical parameters that influence model performance. From the review, the prevailing models primarily focus on binary classification while disregarding cross-dataset validations and multi-stage classification. This gap challenges the delivering effective treatments, especially considering potential drug resistance. Established protocols and classification models are needed to anticipate the specific malaria species. The keywords in automated malaria diagnosis that we identified include machine learning, deep learning, transfer learning, and convolutional neural networks. Through examinations of the constraints in current methodologies, we provide valuable suggestions that could propel the field of automated malaria diagnosis. This systematic review provides a comprehensive overview, critical insights, and a roadmap for future research endeavours in this vital domain of healthcare.