Brain tumors classification with deep learning using data augmentation

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dc.authoridKarakis, Rukiye/0000-0002-1797-3461
dc.authoridGURKAHRAMAN, KALI/0000-0002-0697-125X
dc.contributor.authorGurkahraman, Kali
dc.contributor.authorKarakis, Rukiye
dc.date.accessioned2024-10-26T18:00:27Z
dc.date.available2024-10-26T18:00:27Z
dc.date.issued2021
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractMedical image classification is the process of separating data into a specified number of classes. In recent years, Magnetic Resonance Imaging (MRI) has been widely used in the detection and diagnosis of brain tumors. In this study, it was aimed to classify three different brain tumors (glioma, meningioma and pituitary) using convolutional neural network (CNN) on T1-weighted MR images and to determine the efficiency of axial, coronal and sagittal MR planes in classification. The weights were initialized by transferring to CNN from DenseNet121 network, which was previously trained with ImageNet dataset. In addition, data augmentation was performed on MR images using affine and pixel-level transformations. The features obtained from the first fully connected layer of the trained CNN were also classified by support vector machine (SVM), k nearest neighbor (kNN), and Bayes methods. The performances of these classifiers were measured by the sensitivity, specificity, accuracy, area under curve, and the Pearson correlation coefficient on the test dataset. The accuracy values of the developed CNN and CNN-based SVM, kNN, and Bayes classifiers are 0.9860, 0.9979, 0.9907, and 0.8933, respectively. The CNN-based SVM model proposed for brain tumor classification obtained higher performance values than similar studies in the literature. In addition, coronal plane of the brain was found to give better results than other planes in determining the tumor type.
dc.identifier.doi10.17341/gazimmfd.762056
dc.identifier.endpage1011
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85104348250
dc.identifier.scopusqualityQ2
dc.identifier.startpage997
dc.identifier.trdizinid494465
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.762056
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/494465
dc.identifier.urihttps://hdl.handle.net/20.500.12418/27684
dc.identifier.volume36
dc.identifier.wosWOS:000626722500029
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isotr
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBrain tumor classification
dc.subjectdeep learning
dc.subjectdata augmentation
dc.subjectfeature extraction
dc.subjectsupport vector machine
dc.titleBrain tumors classification with deep learning using data augmentation
dc.typeArticle

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