Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images.

Journal: Journal of glaucoma
Published Date: Sep 1, 2021

Abstract

PURPOSE: (1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence tomography (SS-OCT) images. (2) To assess the performance of DL-based fusion methods in diagnosing glaucoma using a variety of wide-field SS-OCT images and compare their diagnostic abilities with that of conventional parameter-based methods.

Authors

  • Younji Shin
    Department of Electrical Engineering, Hanyang University.
  • Hyunsoo Cho
    Department of Ophthalmology, Hanyang University College of Medicine.
  • Hyo Chan Jeong
    Department of Ophthalmology, Hanyang University Seoul Hospital, Seoul.
  • Mincheol Seong
    Department of Ophthalmology, Hanyang University College of Medicine.
  • Jun-Won Choi
    Department of Electrical Engineering, Hanyang University.
  • Won June Lee
    Department of Ophthalmology, Hanyang University College of Medicine.

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