Mammographic mass classification using wavelet based support vector machine

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dc.contributor.authorGörgel, Pelin
dc.contributor.authorSertbaş, Ahmet
dc.contributor.authorKılıç, Niyazi
dc.contributor.authorUçan, Osman N.
dc.contributor.authorOsman, Onur
dc.date.accessioned2020-11-21T15:53:52Z
dc.date.available2020-11-21T15:53:52Z
dc.date.issued2009en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractIn this paper, we investigate an approach for classification of mammographic masses as benign or malign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-based subband image decomposition. Decision making was performed in two stages as feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. SVM, a learning machine based on statistical learning theory, was trained through supervised learning to classify masses. The research involved 66 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. Preliminary test on mammogram showed over 84.8% classification accuracy by using the SVM with Radial Basis Function (RBF) kernel. Also confusion matrix, accuracy, sensitivity and specificity analysis with different kernel types were used to show the classification performance of SVM.en_US
dc.identifier.endpage875en_US
dc.identifier.issn1303-0914
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-68949105817en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage867en_US
dc.identifier.trdizinid114195en_US
dc.identifier.urihttps://hdl.handle.net/11467/3707
dc.identifier.volume9en_US
dc.identifier.wosWOS:000409740100012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofIstanbul University - Journal of Electrical and Electronics Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectK-fold cross validationen_US
dc.subjectKeywords discrete wavelet transformen_US
dc.subjectMammographic mass classificationen_US
dc.subjectSupport vector machineen_US
dc.titleMammographic mass classification using wavelet based support vector machineen_US
dc.typeArticleen_US

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