Automated multi-class classification for prediction of tympanic membrane changes with deep learning models.

Journal: PloS one
PMID: 36215265

Abstract

BACKGROUNDS AND OBJECTIVE: Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks.

Authors

  • Yeonjoo Choi
    Department of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Jihye Chae
    Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Keunwoo Park
    Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Jaehee Hur
    Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Jihoon Kweon
    Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. kjihoon2@naver.com.
  • Joong Ho Ahn
    Department of Otorhinolaryngology-Head and Neck Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

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