Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

Journal: Head & neck
PMID: 37837264

Abstract

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification.

Authors

  • Zhenzhen You
  • Botao Han
    Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
  • Zhenghao Shi
    Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
  • Minghua Zhao
    School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.
  • Shuangli Du
    School of Computer Science and Engineering, Xi'an University of Technology, China.
  • Jing Yan
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Haiqin Liu
    Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China.
  • Xinhong Hei
    School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Xiaoyong Ren
    Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China.
  • Yan Yan
    Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA.

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