Performance of SVM and ANFIS for Classiﬁcation of Malaria Parasite and Its Life-Cycle-Stages in Blood Smear

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DC Field	Value	Language
dc.contributor.author	Rosnelly, Rika	-
dc.date.accessioned	2019-05-10T03:53:02Z	-
dc.date.available	2019-05-10T03:53:02Z	-
dc.date.issued	2016-10-05	-
dc.identifier.uri	http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3311	-
dc.description	peer review	en_US
dc.description.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%.	en_US
dc.subject	Geometry Texture	en_US
dc.subject	GLCM RBF	en_US
dc.title	Performance of SVM and ANFIS for Classiﬁcation of Malaria Parasite and Its Life-Cycle-Stages in Blood Smear	en_US
dc.type	Other	en_US
Appears in Collections:	Peer Review

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