Comparison of machine learning techniques on MS lesion segmentation

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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.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümü
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.
dc.description.sponsorshipBiyomedikal ve Klinik Mühendisliği Derneğien_US
dc.description.sponsorshipİzmir Kâtip Çelebi Üniversitesien_US
dc.description.sponsorshipBiyomedikal Mühendisliği Bölümüen_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.2019.8895202
dc.identifier.doi10.1109/TIPTEKNO.2019.8895202
dc.identifier.isbn9781728124209
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/TIPTEKNO.2019.8895202
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4619
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofMedical Technologies Congress (TIPTEKNO)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectImage Segmentation
dc.subjectK Means
dc.subjectMRI
dc.subjectMultiple Sclerosis
dc.subjectSupport Vector Machine
dc.titleComparison of machine learning techniques on MS lesion segmentation
dc.typeConference Object

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