A Systematic Review on Automatic Detection of Plasmodium Parasite

• Main Authors: Sumi, A.S., Nugroho, H.A., Hartanto, R.
• Format: Article PeerReviewed
• Published: 2021
• Subjects: Signal Processing; Medical Parasitology; Electrical and Electronic Engineering
• Online Access: https://repository.ugm.ac.id/280454/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104272232&doi=10.46604%2fIJETI.2021.6094&partnerID=40&md5=03e06a2e2c33de20d22ef04b0f3916ed; https://doi.org/10.46604/ijeti.2021.6094
• Institution: Universitas Gadjah Mada

Plasmodium parasite is the main cause of malaria which has taken many lives. Some research works have been conducted to detect the Plasmodium parasite automatically. This research aims to identify the development of current research in the area of Plasmodium parasite detection. The research uses a systematic literature review (SLR) approach comprising three stages, namely planning, conducting, and reporting. The search process is based on the keywords which were determined in advance. The selection process involves the inclusion and exclusion criteria. The search yields 45 literatures from five different digital libraries. The identification process finds out that 28 methods are applied and mainly categorizes as machine learning algorithms with performance achievements between 60 and 95. Overall, the research of Plasmodium parasite detection today has focused on the development with artificial intelligence specifically related to machine and deep learning. These approaches are believed as the most effective approach to detect Plasmodium parasites. © by the authors. Licensee TAETI, Taiwan. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC) license (http://creativecommons.org/licenses/by/4.0/).