A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study

Atici M. A., SAĞIROĞLU Ş., Celtikci P., UÇAR M., BÖRCEK A. Ö., Emmez H., ...More

TURKISH NEUROSURGERY, vol.30, no.2, pp.199-205, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.5137/1019-5149.jtn.27106-19.2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.199-205
  • Keywords: Artificial intelligence, Deep learning, Glioma, Machine learning, Magnetic resonance imaging
  • Gazi University Affiliated: Yes


AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans.