Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.

Journal: The Laryngoscope
PMID:

Abstract

OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings.

Authors

  • Jianjun Ren
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Xueping Jing
    Department of Automation, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Xue Ren
    Department of Pediatrics, Jinan Municipal Hospital of Traditional Chinese Medicine, Jinan, 250012, China. higher0314@163.com.
  • Yang Xu
    Dermatological Department, Nan Chong Center Hospital, Nanchong, China.
  • Qiuyun Yang
    Department of Forensics, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, China.
  • Lanzhi Ma
    Department of Preclinical Medicine, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, China.
  • Yi Sun
    Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Ning Yang
    Department of Cardiology, Tianjin Chest Hospital, No 261, Taierzhuang South road, Jinnan district, Tianjin, 300222, China.
  • Jian Zou
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Yongbo Zheng
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Min Chen
    School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.
  • Weigang Gan
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Ting Xiang
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Junnan An
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Ruiqing Liu
    Department of Otorhinolaryngology, Kunming City Women and Children Hospital, Kunming, China.
  • Cao Lv
    Department of Otorhinolaryngology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Ken Lin
    Department of Otorhinolaryngology, The Affiliated Children's Hospital of Kunming Medical University, Kunming, China.
  • Xianfeng Zheng
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Fan Lou
    Department of Otorhinolaryngology, The Affiliated Children's Hospital of Kunming Medical University, Kunming, China.
  • Yufang Rao
    Department of Otorhinolaryngology, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
  • Hui Yang
    Department of Neurology, The Second Affiliated Hospital of Guizhou University of Chinese Medicine, Guiyang, China.
  • Kai Liu
    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China.
  • Geoffrey Liu
    Medical Oncology and Medical Biophysics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
  • Tao Lu
    Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China.
  • Xiujuan Zheng
    Department of Automation, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, China.
  • Yu Zhao
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.