Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Development of Deep Learning-based Clinical Decision Supporting Technique for Laryngeal Disease using Endoscopic Images

딥러닝 기반 후두부 질환 내시경 영상판독 보조기술 개발

  • Jung, In Ho (Interdisciplinary Program in Biomedical Engineering, College of Medicine, Pusan National University) ;
  • Hwang, Young Jun (Interdisciplinary Program in Biomedical Engineering, College of Medicine, Pusan National University) ;
  • Sung, Eui-Suk (Department of Otolaryngology-Head and Neck Surgery, College of Medicine, Pusan National University) ;
  • Nam, Kyoung Won (Interdisciplinary Program in Biomedical Engineering, College of Medicine, Pusan National University)
  • 정인호 (부산대학교 의과대학 의공학협동과정) ;
  • 황영준 (부산대학교 의과대학 의공학협동과정) ;
  • 성의숙 (부산대학교 의과대학 이비인후과학교실) ;
  • 남경원 (부산대학교 의과대학 의공학협동과정)
  • Received : 2021.12.23
  • Accepted : 2022.04.11
  • Published : 2022.04.30
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Abstract

Purpose: To propose a deep learning-based clinical decision support technique for laryngeal disease on epiglottis, tongue and vocal cords. Materials and Methods: A total of 873 laryngeal endoscopic images were acquired from the PACS database of Pusan N ational University Yangsan Hospital. and VGG16 model was applied with transfer learning and fine-tuning. Results: The values of precision, recall, accuracy and F1-score for test dataset were 0.94, 0.97, 0.95 and 0.95 for epiglottis images, 0.91, 1.00, 0.95 and 0.95 for tongue images, and 0.90, 0.64, 0.73 and 0.75 for vocal cord images, respectively. Conclusion: Experimental results demonstrated that the proposed model have a potential as a tool for decision-supporting of otolaryngologist during manual inspection of laryngeal endoscopic images.

Keywords

Acknowledgement

This study was supported by a 2021 research grant from Pusan National University Yangsan Hospital.

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