Oral mucosal lesions triage via YOLOv7 models.

Journal: Journal of the Formosan Medical Association = Taiwan yi zhi
Published Date:

Abstract

BACKGROUND/PURPOSE: The global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions.

Authors

  • Yu Hsu
    Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
  • Cheng-Ying Chou
    Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.
  • Yu-Cheng Huang
    Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
  • Yu-Chieh Liu
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Yong-Long Lin
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Zi-Ping Zhong
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Jun-Kai Liao
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Jun-Ching Lee
    Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan.
  • Hsin-Yu Chen
    Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan.
  • Jang-Jaer Lee
    Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan; Department of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address: leejj@ntuh.gov.tw.
  • Shyh-Jye Chen
    Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address: james_5586@hotmail.com.