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Performance comparison of machine learning algorithms for malaria detection using microscopic images

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.

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