Vocal cord lesions classification based on deep convolutional neural network and transfer learning.

Journal: Medical physics
Published Date: Dec 8, 2021

Abstract

PURPOSE: Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer-aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning.

Authors

  • Qian Zhao
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yuqing He
    School of Transportation, Fujian University of Technology, Fuzhou 350118, China.
  • Yanda Wu
    Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
  • Dongyan Huang
    National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Cai Sun
    National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
  • Jun Ju
    National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
  • Jiasen Wang
    National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China.
  • Jeremy Jianshuo-Li Mahr
    Division of Life Sciences, Rutgers, The State University of New Jersey, New Jersey, USA.

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