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


Alis D. C. , Bagcilar O., Senli Y. D. , İŞLER C., Yergin M., Kocer N., ...More

CLINICAL RADIOLOGY, vol.75, no.5, pp.351-357, 2020 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Editorial Material
  • Volume: 75 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.1016/j.crad.2019.12.008
  • Title of Journal : CLINICAL RADIOLOGY
  • Page Numbers: pp.351-357

Abstract

AIM: 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).