Publication Type : Journal Article
Publisher : IJRAR19RP014 International Journal of Research and Analytical Reviews (IJRAR), IJRAR
Source : IJRAR19RP014 International Journal of Research and Analytical Reviews (IJRAR), IJRAR, Volume 6, Issue 1 (2019)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Malaria is a blood-borne disease by mosquito caused by Plasmodium parasites. The standard method for malaria detection involves preparing a blood smear and examining the stained blood smear using a microscope to detect the parasite genus Plasmodium, which heavily relies on the expertise of trained experts. Under the roof of this paper, with the intention of singling out the parasite blood smears for malaria detection, shallow machine learning algorithms are used against the traditional method, which has some snags related to sensitivity and specificity. The proposed methodology determines the malarial infection with the help of captured images of patients without staining the blood or need of experts.
Cite this Research Publication : G. B. Saiprasath, Babu, N., ArunPriyan, J., Vinayakumar, R., Sowmya, V., and Dr. Soman K. P., “Performance comparison of machine learning algorithms for malaria detection using microscopic images”, IJRAR19RP014 International Journal of Research and Analytical Reviews (IJRAR), vol. 6, no. 1, 2019.