Hsu Yu, Chou Cheng-Ying, Huang Yu-Cheng, Liu Yu-Chieh, Lin Yong-Long, Zhong Zi-Ping, Liao Jun-Kai, Lee Jun-Ching, Chen Hsin-Yu, Lee Jang-Jaer, Chen Shyh-Jye
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 Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
J Formos Med Assoc. 2024 Jul 12. doi: 10.1016/j.jfma.2024.07.010.
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.
A dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage.
The YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification.
The study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.
背景/目的：唇癌和口腔癌的全球发病率持续上升，因此需要改进早期检测方法。本研究利用计算机视觉和深度学习的能力，以加强口腔黏膜病变的早期检测和分类。
一个最初由2006年至2013年收集的6903张白光宏观图像组成的数据集被扩展到超过50,000张图像，用于训练YOLOv7深度学习模型。病变被分为三个转诊等级：良性（绿色）、潜在恶性（黄色）和恶性（红色），以促进高效分诊。
YOLOv7模型，特别是YOLOv7-E6，在所有病变类别中都表现出高精度和召回率。YOLOv7-D6模型在识别恶性病变方面表现出色，具有显著的精度、召回率和F1分数。包括在YOLOv7-D6-CA模型中整合坐标注意力在内的改进措施，显著提高了病变分类的准确性。
该研究强调了在口腔病变分类分诊中对各种YOLOv7模型配置进行有力比较。总体结果突出了深度学习模型在口腔癌早期检测中的潜力，为临床环境和远程筛查应用提供了有价值的工具。
