Self-Attention Mechanisms-Based Laryngoscopy Image Classification Technique for Laryngeal Cancer Detection.

Journal: Head & neck
PMID: 39526389

Abstract

BACKGROUND: The early diagnosis of laryngeal cancer (LCA) is crucial for prognosis, driving our search for an accurate, precise, and sensitive deep learning model to assist in LCA detection.

Authors

  • Yi-Fan Kang
    Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
  • Lie Yang
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, 637460, Singapore.
  • Yi-Fan Hu
    Department of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
  • Kai Xu
    Department of Anesthesiology, Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huaian, China.
  • Lan-Jun Cai
    Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
  • Bin-Bin Hu
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, 637460, Singapore.
  • Xiang Lu
    Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. Electronic address: luxiangent@hotmail.com.

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