Evaluation of deep convolutional neural networks for glaucoma detection.

Journal: Japanese journal of ophthalmology
Published Date: Feb 24, 2019

Abstract

PURPOSE: To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images STUDY DESIGN: A retrospective study PATIENTS AND METHODS: To investigate the discriminative ability of 3 DCNNs, we used a total of 3312 images consisting of 369 images from glaucoma-confirmed eyes, 256 images from glaucoma-suspected eyes diagnosed by a glaucoma expert, and 2687 images judged to be nonglaucomatous eyes by a glaucoma expert. We also investigated the effects of image size on the discriminative ability and heatmap analysis to determine which parts of the image contribute to the discrimination. Additionally, we used 465 poor-quality images to investigate the effect of poor image quality on the discriminative ability.

Authors

  • Sang Phan
    Research Center for Medical Bigdata (RCMB), National Institute of Informatics, Tokyo, Japan.
  • Shin'ichi Satoh
    Research Center for Medical Bigdata (RCMB), National Institute of Informatics, Tokyo, Japan.
  • Yoshioki Yoda
    Yamanashi Koseiren Health Care Center, Kofu, Japan.
  • Kenji Kashiwagi
    Department of Ophthalmology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan. kenjik@yamanashi.ac.jp.
  • Tetsuro Oshika
    Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

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