Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models

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dc.contributor.authorUnal, Yavuz
dc.contributor.authorPolat, Kemal
dc.contributor.authorKocer, H. Erdinc
dc.contributor.authorHariharan, M.
dc.date.accessioned2020-03-26T19:01:43Z
dc.date.available2020-03-26T19:01:43Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractDisc abnormalities cause a great number of complaints including lower back pain. Lower back pain is one of the most common types of pain in the world. The computer-assisted detection of this ailment will be of great use to physicians and specialists. With this study, hybrid models have been developed which include feature extraction, selection and classification characteristics for the purpose of determining the disc abnormalities in the lumbar region. In determining the abnormalities, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people. In the feature extraction stage, 27 appearance characteristics and form characteristics were acquired from both sagittal and transverse images. In the feature selection stage, the F-Score-Based Feature Selection (FSFS) and the Correlation-Based Feature Selection (CBFS) methods were used to select the best discriminative features. The number of features was reduced to 5 from 27 by using the FSFS, and to 22 from 27 by using the CBFS. In the last stage, five different classification algorithms, i.e. the Multi-Layer Perceptron, the Support Vector Machine, the Decision Tree, the Naive Bayes, and the k Nearest Neighbor algorithms were applied. In addition, the combination of the classifier model (the combination of the bagging and the random forests) has been used to improve the classification performance in the detection of lumbar disc datasets. The results which were obtained suggest that the proposed hybrid models can be used safely in detecting the disc abnormalities. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2015.04.031en_US
dc.identifier.endpage76en_US
dc.identifier.issn1568-4946en_US
dc.identifier.issn1872-9681en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage65en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2015.04.031
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31986
dc.identifier.volume33en_US
dc.identifier.wosWOS:000355262900006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectLumbar disc abnormalityen_US
dc.subjectLumbar MRIen_US
dc.subjectLumbar spineen_US
dc.subjectHybrid modelsen_US
dc.subjectHybrid featuresen_US
dc.subjectFeature selectionen_US
dc.titleDetection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid modelsen_US
dc.typeArticleen_US

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