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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Elsevier
2025-07-01
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| Series: | Journal of the Formosan Medical Association |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0929664624003139 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Yu Hsu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-27cf55c27e2c467a8dc71d0079c97b3f |
| institution | OA Journals |
| issn | 0929-6646 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of the Formosan Medical Association |
| spelling | doaj-art-27cf55c27e2c467a8dc71d0079c97b3f2025-08-20T02:20:37ZengElsevierJournal of the Formosan Medical Association0929-66462025-07-01124762162710.1016/j.jfma.2024.07.010Oral mucosal lesions triage via YOLOv7 modelsYu Hsu0Cheng-Ying Chou1Yu-Cheng Huang2Yu-Chieh Liu3Yong-Long Lin4Zi-Ping Zhong5Jun-Kai Liao6Jun-Ching Lee7Hsin-Yu Chen8Jang-Jaer Lee9Shyh-Jye Chen10Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, TaiwanDepartment of Biomechatronics Engineering, National Taiwan University, Taipei, TaiwanDepartment of Medical Imaging, National Taiwan University Hospital, Taipei, TaiwanDepartment of Biomechatronics Engineering, National Taiwan University, Taipei, TaiwanDepartment of Biomechatronics Engineering, National Taiwan University, Taipei, TaiwanDepartment of Biomechatronics Engineering, National Taiwan University, Taipei, TaiwanDepartment of Biomechatronics Engineering, National Taiwan University, Taipei, TaiwanDepartment of Dentistry, National Taiwan University Hospital, Taipei, TaiwanDepartment of Dentistry, National Taiwan University Hospital, Taipei, TaiwanDepartment of Dentistry, National Taiwan University Hospital, Taipei, Taiwan; Department of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan; Corresponding author. Department of Dentistry, National Taiwan University Hospital, No. 7, Chung-Shan S. Rd., Taipei City, 10002, Taiwan.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; Corresponding author. Department of Medical Imaging, National Taiwan University Hospital, No. 7, Chung-Shan S. Rd., Taipei City, 10002, Taiwan.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.http://www.sciencedirect.com/science/article/pii/S0929664624003139Mouth neoplasmsOral pathologyDeep learningArtificial intelligenceComputer assisted decision making |
| spellingShingle | 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 Oral mucosal lesions triage via YOLOv7 models Journal of the Formosan Medical Association Mouth neoplasms Oral pathology Deep learning Artificial intelligence Computer assisted decision making |
| title | Oral mucosal lesions triage via YOLOv7 models |
| title_full | Oral mucosal lesions triage via YOLOv7 models |
| title_fullStr | Oral mucosal lesions triage via YOLOv7 models |
| title_full_unstemmed | Oral mucosal lesions triage via YOLOv7 models |
| title_short | Oral mucosal lesions triage via YOLOv7 models |
| title_sort | oral mucosal lesions triage via yolov7 models |
| topic | Mouth neoplasms Oral pathology Deep learning Artificial intelligence Computer assisted decision making |
| url | http://www.sciencedirect.com/science/article/pii/S0929664624003139 |
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