Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network.

Journal: Gastrointestinal endoscopy
Published Date: Jul 1, 2020

Abstract

BACKGROUND AND AIMS: Protruding lesions of the small bowel vary in wireless capsule endoscopy (WCE) images, and their automatic detection may be difficult. We aimed to develop and test a deep learning-based system to automatically detect protruding lesions of various types in WCE images.

Authors

  • Hiroaki Saito
    Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan.
  • Tomonori Aoki
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Kazuharu Aoyama
    AI Medical Service Inc Tokyo Japan.
  • Yusuke Kato
    AI Medical Service Inc., Tokyo, Japan.
  • Akiyoshi Tsuboi
    Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan.
  • Atsuo Yamada
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Mitsuhiro Fujishiro
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Endoscopy and Endoscopic Surgery, The University of Tokyo, Tokyo, Japan.
  • Shiro Oka
    Department of Gastroenterology & Metabolism, Hiroshima University Hospital, Hiroshima, Japan.
  • Soichiro Ishihara
    Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan; Surgery Department, Sanno Hospital, International University of Health and Welfare, Tokyo, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Tomoki Matsuda
    Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan.
  • Masato Nakahori
    Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan.
  • Shinji Tanaka
    Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan.
  • Kazuhiko Koike
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Tomohiro Tada
    AI Medical Service Inc Tokyo Japan.

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