Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination.

Journal: Computers in biology and medicine
Published Date: Jul 8, 2020

Abstract

AIM: The aim of this study was to determine whether our deep convolutional neural network-based anomaly detection model can distinguish differences in esophagus images and stomach images obtained from gastric X-ray examinations.

Authors

  • Ren Togo
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.
  • Haruna Watanabe
    Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-Ku, Sapporo, 060-0814, Japan. Electronic address: haruna@lmd.ist.hokudai.ac.jp.
  • Takahiro Ogawa
    Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
  • Miki Haseyama
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.

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