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Automatic Malaria Parasite Detection and Classification using ANFIS

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dc.contributor.author Osman, Hosam Hatim
dc.date.accessioned 2017-04-27T06:56:45Z
dc.date.available 2017-04-27T06:56:45Z
dc.date.issued 2016-12-10
dc.identifier.citation Osman, Hosam Hatim . Automatic Malaria Parasite Detection and Classification using ANFIS / Hosam Hatim Osman ; Fragoon Mohamed Ahmed .- Khartoum: Sudan University of Science and Technology, college of Engineering, 2016 .-79p. :ill. ;28cm .- M.Sc. en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/16778
dc.description Thesis en_US
dc.description.abstract Recent advancement in genomic technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. Computer based malarial parasite analysis and classification has opened a new area for the early malaria detection that showed potential to overcome the drawbacks of manual strategies. This thesis presents a method for automatic classification of malarial infected cells. Blood cell segmentation and morphological analysis is a challenging due complexity of the blood cells. To improve the performance of malaria parasite segmentation and classification, we have used different set of features which are forward to the ANFIS classifier for malaria classification. the segmentation of clustered partially overlapping objects with a shape initially separated using marker controlled watershed segmentation accompanied with and overlapping cells concave point segmentation and contours are approximated using an ellipse. whereas ANFIS classifier for classification on different set of texture and shape features. This Study shows 96.33% and 96.31% recognition rates for both training and testing using ANFIS classifier. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Biomedical Engineering en_US
dc.subject Classification using ANFIS en_US
dc.subject Malaria Parasite Detection en_US
dc.title Automatic Malaria Parasite Detection and Classification using ANFIS en_US
dc.title.alternative كشف وتصنيف طفيل الملاريا أليا باستخدام الانفيس en_US
dc.type Thesis en_US
dc.contributor.Supervisor Supervisor,- Fragoon Mohamed Ahmed


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