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dc.contributor.authorDoğan, Ahsen Feyza
dc.contributor.authorGöksel Duru, Dilek
dc.date.accessioned2019-12-23T13:36:35Z
dc.date.available2019-12-23T13:36:35Z
dc.date.issued2019en_US
dc.identifier.citationDoğan, A. F. ve Göksel Duru, D. (2019). Comparison of machine learning techniques on MS lesion segmentation. Medical Technologies Congress (TIPTEKNO) içinde (393-396. ss.). Izmir, Turkey, 3-5 October 2019. https://doi.org/10.1109/TIPTEKNO.2019.8895202en_US
dc.identifier.isbn9781728124209
dc.identifier.urihttps://doi.org/10.1109/TIPTEKNO.2019.8895202
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4619
dc.description.abstractMultiple sclerosis arises with conformational change in myelin sheath. Magnetic resonance imaging is frequently used in detection of MS. In this study, to figure out MS lesion, machine learning techniques, namely k means and support vector machine are used. K means is an unsupervised technique used to cluster data into k groups. Support vector machine is a supervised machine learning technique used as classifier. Since dataset does not contain label of images, labels are generated by pixel values adopted from original MR image. Classification results were achieved as 70.24% and 91.04% for k means and SVM respectively. According to the promising results, future research will focus on the automatization of this segmentation process via deep learning leading to medical decision support system.en_US
dc.description.sponsorshipBiyomedikal ve Klinik Mühendisliği Derneğien_US
dc.description.sponsorshipİzmir Kâtip Çelebi Üniversitesien_US
dc.description.sponsorshipBiyomedikal Mühendisliği Bölümüen_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectImage Segmentationen_US
dc.subjectK Meansen_US
dc.subjectMRIen_US
dc.subjectMultiple Sclerosisen_US
dc.subjectSupport Vector Machineen_US
dc.titleComparison of machine learning techniques on MS lesion segmentationen_US
dc.typeconferenceObjecten_US
dc.relation.ispartofMedical Technologies Congress (TIPTEKNO)en_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/TIPTEKNO.2019.8895202en_US


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