Classification of optical coherence tomography images using a capsule network.

Journal: BMC ophthalmology
Published Date: Mar 19, 2020

Abstract

BACKGROUND: Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional relations in a pooling layer. Therefore, because capsule networks can learn positional information from images, we attempted application of a capsule network to OCT images to overcome that shortcoming. This study is our attempt to improve classification accuracy by replacing CNN with a capsule network.

Authors

  • Takumasa Tsuji
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Yuta Hirose
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Kohei Fujimori
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Takuya Hirose
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Asuka Oyama
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Yusuke Saikawa
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Tatsuya Mimura
    Department of Ophthalmology, Teikyo University School of Medicine, Tokyo, Japan.
  • Kenshiro Shiraishi
    Department of Radiology, Teikyo University School of Medicine, Tokyo, Japan.
  • Takenori Kobayashi
    Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.
  • Atsushi Mizota
    Department of Ophthalmology, Teikyo University School of Medicine, Tokyo, Japan.
  • Jun'ichi Kotoku
    Graduate School of Medical Technology, Teikyo University.

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