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The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas

dc.contributor.authorYergin, M.
dc.contributor.authorKizilkilic, O.
dc.contributor.authorİŞLER, Cihan
dc.contributor.authorKocer, N.
dc.contributor.authorIslak, C.
dc.contributor.authorAlis, D.
dc.contributor.authorBagcilar, O.
dc.contributor.authorSenli, Y. D.
dc.date.accessioned2021-03-02T18:33:55Z
dc.date.available2021-03-02T18:33:55Z
dc.date.issued2020
dc.identifier.citationAlis D., Bagcilar O., Senli Y. D. , İŞLER C., Yergin M., Kocer N., Islak C., Kizilkilic O., "The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas", CLINICAL RADIOLOGY, cilt.75, ss.351-357, 2020
dc.identifier.issn0009-9260
dc.identifier.othervv_1032021
dc.identifier.otherav_de9e578c-b793-4adf-8c74-ebb5668ccb92
dc.identifier.urihttp://hdl.handle.net/20.500.12627/5163
dc.identifier.urihttps://doi.org/10.1016/j.crad.2019.12.008
dc.description.abstractAIM: To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG).
dc.language.isoeng
dc.subjectNükleer Tıp
dc.subjectDahili Tıp Bilimleri
dc.subjectSağlık Bilimleri
dc.subjectTıp
dc.subjectKlinik Tıp (MED)
dc.subjectKlinik Tıp
dc.subjectRADYOLOJİ, NÜKLEER TIP ve MEDİKAL GÖRÜNTÜLEME
dc.titleThe diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas
dc.typeMakale
dc.relation.journalCLINICAL RADIOLOGY
dc.contributor.departmentMehmet Akif Ersoy Thoracic Cardiovascular Surgery Education Research Hospital , ,
dc.identifier.volume75
dc.identifier.issue5
dc.identifier.startpage351
dc.identifier.endpage357
dc.contributor.firstauthorID2281034


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