Effects of covariates on classification of bipolar disorder using structural MRI [Kovaryantlarin yapisal MRI görüntüler kullanilarak yapilan bipolar bozukluk siniflandirilmasina etkileri]

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dc.contributor.authorCigdem O.
dc.contributor.authorHoruz E.
dc.contributor.authorSoyak R.
dc.contributor.authorAydeniz B.
dc.contributor.authorSulucay A.
dc.contributor.authorOguz K.
dc.contributor.authorDemirel H.
dc.contributor.authorKitis O.
dc.contributor.authorEker C.
dc.contributor.authorGonul A.S.
dc.contributor.authorUnay D.
dc.date.accessioned2019-10-26T21:12:22Z
dc.date.available2019-10-26T21:12:22Z
dc.date.issued2019
dc.departmentEge Üniversitesien_US
dc.description.abstractThree-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection and diagnosis of neuroanatomical abnormalities, including bipolar disorder (BD). Pre-processing of 3D-MRI scans plays an important role in post-processing. In this study, Voxel-Based Morphometry (VBM) is used to compare the morphological differences at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs). The effects of using different covariates (i.e. total intracranial volume (TIV), age, sex, and their combinations) on classification of BDs from HCs have been investigated for GM-only, WM-only, and their combination. 3D masks for GM and WM are generated separately by using local differences between BPs and HCs and the two sample t-test method. Principle component analysis based dimensionality reduction and support vector machine with Gaussian kernel are employed for classification of 26 BDs and 38 HCs obtained from Ege University, School of Medicine, Department of Psychiatry. The results indicate that using only TIV as a covariate provides more robust results for BD classification compared to other covariate combinations. Furthermore, the combination of GM and WM improves classification performance. The highest classification accuracies obtained for GM, WM, and their combination are 70.30, 79.70, and 82.80 respectively. © 2019 IEEE.en_US
dc.identifier.doi10.1109/EBBT.2019.8741586
dc.identifier.isbn9781728110134
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/EBBT.2019.8741586
dc.identifier.urihttps://hdl.handle.net/11454/15679
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBipolar disorderen_US
dc.subjectCAT12en_US
dc.subjectPCAen_US
dc.subjectSPM12en_US
dc.subjectSVMen_US
dc.titleEffects of covariates on classification of bipolar disorder using structural MRI [Kovaryantlarin yapisal MRI görüntüler kullanilarak yapilan bipolar bozukluk siniflandirilmasina etkileri]en_US
dc.typeConference Objecten_US

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