Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database.

Journal: EBioMedicine
Published Date:

Abstract

BACKGROUND: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment.

Authors

  • Dongchul Cha
    Department of Otorhinolaryngology, Yonsei University College of Medicine, Republic of Korea.
  • Chongwon Pae
    Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Republic of Korea; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Republic of Korea; Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Si-Baek Seong
    Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Republic of Korea; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Republic of Korea.
  • Jae Young Choi
    Image and Video Systems Laboratory, Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-Gu, Daejeon 305-701, Republic of Korea; Department of Biomedical Engineering, Jungwon University, 85 Munmu-Ro Goesan-Eup Goesan-Gun, Chungcheongbuk-Do 367-805, Republic of Korea.
  • Hae-Jeong Park
    BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Radiology, Department of Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address: parkhj@yonsei.ac.kr.