Brain tumor classification using the fused features extracted from expanded tumor region

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dc.authorscopusid56780068800
dc.authorscopusid35617556600
dc.authorscopusid55666247200
dc.contributor.authorÖksüz, Coşku
dc.contributor.authorUrhan, Oğuzhan
dc.contributor.authorGüllü, Mehmet Kemal
dc.date.accessioned2022-02-15T16:58:13Z
dc.date.available2022-02-15T16:58:13Z
dc.date.issued2022
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractIn this study, a brain tumor classification method using the fusion of deep and shallow features is proposed to distinguish between meningioma, glioma, pituitary tumor types and to predict the 1p/19q co-deletion status of LGG tumors. Brain tumors can be located in a different region of the brain, and the texture of the surrounding tissues may also vary. Therefore, the inclusion of surrounding tissues into the tumor region (ROI expansion) can make the features more distinctive. In this work, pre-trained AlexNet, ResNet-18, GoogLeNet, and ShuffleNet networks are used to extract deep features from the tumor regions including its surrounding tissues. Even though the deep features are extremely important in classification, some low-level information regarding tumors may be lost as the network deepens. Accordingly, a shallow network is designed to learn low-level information. Next, in order to compensate the information loss, deep features and shallow features are fused. SVM and k-NN classifiers are trained using the fused feature sets. Experimental results achieved on two publicly available data sets demonstrate that using the feature fusion and the ROI expansion at the same time improves the average sensitivity by about 11.72% (ROI expansion: 8.97%, feature fusion: 2.75%). These results confirm the assumption that the tissues surrounding the tumor region carry distinctive information. Not only that, the missing low-level information can be compensated thanks to the feature fusion. Moreover, competitive results are achieved against state-of-the-art studies when the ResNet-18 is used as the deep feature extractor of our classification framework.en_US
dc.identifier.doi10.1016/j.bspc.2021.103356
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85120963549en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.103356
dc.identifier.urihttps://hdl.handle.net/20.500.14034/367
dc.identifier.volume72en_US
dc.identifier.wosWOS:000730100300007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.journalBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain tumor classificationen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectFeature extractionen_US
dc.subjectComputer-aided diagnosisen_US
dc.subject1pen_US
dc.subject19q codeletionen_US
dc.subjectMri Imagesen_US
dc.subjectGradeen_US
dc.subject1P/19Qen_US
dc.subjectSegmentationen_US
dc.subjectChemotherapyen_US
dc.subjectInformationen_US
dc.subjectDeletionen_US
dc.titleBrain tumor classification using the fused features extracted from expanded tumor regionen_US
dc.typeArticleen_US

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