Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features

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dc.contributor.authorÇınarer, Gökalp
dc.contributor.authorEmiroğlu, Bülent Gürsel
dc.contributor.authorYurttakal, Ahmet Haşim
dc.date.accessioned2021-01-14T18:10:24Z
dc.date.available2021-01-14T18:10:24Z
dc.date.issued2020
dc.departmentKKÜ
dc.descriptionEmiroglu, Bulent Gursel/0000-0002-1656-6450; Cinarer, Gokalp/0000-0003-0818-6746; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466
dc.description.abstractGliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I-II-III-IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II,n= 77; Grade III,n= 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.en_US
dc.identifier.citationÇinarer G, Emiroğlu BG, Yurttakal AH. Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features. Applied Sciences. 2020; 10(18):6296. https://doi.org/10.3390/app10186296en_US
dc.identifier.doi10.3390/app10186296
dc.identifier.issn2076-3417
dc.identifier.issue18en_US
dc.identifier.urihttps://doi.org/10.3390/app10186296
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12576
dc.identifier.volume10en_US
dc.identifier.wosWOS:000580451100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherMDPIen_US
dc.relation.ispartofAPPLIED SCIENCES-BASEL
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep learningen_US
dc.subjectradiomicsen_US
dc.subjectwaveleten_US
dc.subjectgradingen_US
dc.titlePrediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Featuresen_US
dc.typeArticle

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