Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network.

Journal: Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Published Date: May 1, 2021

Abstract

BACKGROUND: Although great advances in artificial intelligence for interpreting small bowel capsule endoscopy (SBCE) images have been made in recent years, its practical use is still limited. The aim of this study was to develop a more practical convolutional neural network (CNN) algorithm for the automatic detection of various small bowel lesions.

Authors

  • Yunseob Hwang
    Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea.
  • Han Hee Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Chunghyun Park
    Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea.
  • Bayu Adhi Tama
    Department of Mechanical Engineering, Pohang University of Science and Technology, Republic of Korea.
  • Jin Su Kim
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Dae Young Cheung
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Woo Chul Chung
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Young-Seok Cho
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Kang-Moon Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Myung-Gyu Choi
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Seungchul Lee
    Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 223, 5th Engineering Building 77 Cheongam-ro, Nam-Gu, Pohang, Gyeongbuk 37673, Republic of Korea. Electronic address: seunglee@postech.ac.kr.
  • Bo-In Lee
    Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Keywords

External Resources

Popular Topics
Recent Journals