Oral mucosal lesions triage via YOLOv7 models

Oral mucosal lesions triage via YOLOv7 models

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. Methods: A d...

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Main Authors: Yu Hsu, Cheng-Ying Chou, Yu-Cheng Huang, Yu-Chieh Liu, Yong-Long Lin, Zi-Ping Zhong, Jun-Kai Liao, Jun-Ching Lee, Hsin-Yu Chen, Jang-Jaer Lee, Shyh-Jye Chen
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Journal of the Formosan Medical Association
Subjects:
Mouth neoplasms
Oral pathology
Deep learning
Artificial intelligence
Computer assisted decision making
Online Access:http://www.sciencedirect.com/science/article/pii/S0929664624003139
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Summary: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. Methods: 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. Results: 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. Conclusion: 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.
ISSN:0929-6646

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