Deep learning-based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy.

Journal: BMC medical imaging
Published Date: Jul 4, 2025

Abstract

BACKGROUND: To evaluate the performance of deep learning models in classifying parotid gland tumors using T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MR images, along with DCE data derived from time-intensity curves.

Authors

  • Kazim Ayberk Sinci
    Department of Radiology, Kanuni Sultan Suleyman Education and Research Hospital, Istanbul, 34303, Türkiye. ayberksinci94@gmail.com.
  • İlker Özgür Koska
    Department of Radiology, Behçet Uz Children's Hospital, İzmir, Turkey.
  • Yusuf Kenan Cetinoglu
    Batman Training and Research Hospital, Department of Radiology, 72070 Batman, Turkey. Electronic address: kenancetinoglu@hotmail.com.
  • Nezahat Erdogan
    Department of Radiology, Faculty of Medicine, Izmir Katip Celebi University, Izmir, Türkiye.
  • Ali Murat Koc
    Floy GmbH, Munich, Germany.
  • Nuket Ozkavruk Eliyatkin
    Department of Pathology, Faculty of Medicine, Izmir Katip Celebi University, Izmir, Türkiye.
  • Çağan Koska
    Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
  • Barkan Candan
    Department of Electrical Electronical Engineering, Yaşar University, Bornova, Izmir, Türkiye.

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