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

Alis, DENİZ; Bagcilar, O.; Senli, Y.; İŞLER, Cihan; Yergin, M.; Kocer, N.; Islak, C.; Kizilkilic, O.

doi:10.1016/j.crad.2019.12.008

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

Atıf İçin Kopyala

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

CLINICAL RADIOLOGY, cilt.75, sa.5, ss.351-357, 2020 (SCI-Expanded)

Yayın Türü: Makale / Editöre Mektup
Cilt numarası: 75 Sayı: 5
Basım Tarihi: 2020
Doi Numarası: 10.1016/j.crad.2019.12.008
Dergi Adı: CLINICAL RADIOLOGY
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, CINAHL, EMBASE, MEDLINE
Sayfa Sayıları: ss.351-357
Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet

Özet

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).