Abstract
A method to classify Plasmodium malaria disease along with its life stage is presented. The geometry and texture features are used as Plasmodium features for classification. The geometry features are area and perimeters. The texture features are computed from GLCM matrices. The support vector machine (SVM) classifier is employed for classifying the Plasmodium and its life stage into 12 classes. Experiments were conducted using 600 images of blood samples. The SVM with RBF kernel yields an accuracy of 99.1%, while the ANFIS gives an accuracy of 88.5%.
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Acknowledgment
The authors would like to thank the Directorate General of Higher Education, the Ministry of Research and Higher Education of the Republic of Indonesia for sponsoring this research. The authors would also like to thank the parasitology Health Laboratory of the North Sumatra Province and Bina Medical Support Services (BPPM), Jakarta, for supporting this research.
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Hartati, S., Harjoko, A., Rosnelly, R., Chandradewi, I., Faizah (2019). Performance of SVM and ANFIS for Classification of Malaria Parasite and Its Life-Cycle-Stages in Blood Smear. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_9
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DOI: https://doi.org/10.1007/978-981-13-3441-2_9
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