Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.

Journal: Endoscopy
Published Date: Aug 23, 2019

Abstract

BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly adopted in clinical practice. This study aimed to establish convolutional neural network (CNN) models to automatically classify gastric neoplasms based on endoscopic images.

Authors

  • Bum-Joo Cho
    Department of Ophthalmology, Hallym University College of Medicine, Chuncheon, Korea.
  • Chang Seok Bang
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Se Woo Park
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Young Joo Yang
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea.
  • Seung In Seo
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Hyun Lim
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Woon Geon Shin
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Ji Taek Hong
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Yong Tak Yoo
    Dudaji Inc., Seoul, Korea.
  • Seok Hwan Hong
    Dudaji Inc., Seoul, Korea.
  • Jae Ho Choi
    Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea.
  • Jae Jun Lee
    Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea.
  • Gwang Ho Baik
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.

Keywords

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