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

Date
2020
Author
Yergin, M.
Kizilkilic, O.
İŞLER, Cihan
Kocer, N.
Islak, C.
Alis, D.
Bagcilar, O.
Senli, Y. D.
Metadata
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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).
URI
http://hdl.handle.net/20.500.12627/5163
https://doi.org/10.1016/j.crad.2019.12.008
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