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Updated: Jul 17, 2026

Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma
Published on: July 25, 2011
Oral mucosal lesions triage via YOLOv7 models
Yu Hsu1, Cheng-Ying Chou2, Yu-Cheng Huang3
1Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Deep learning models like YOLOv7 show promise for early oral cancer detection. These computer vision tools accurately classify oral lesions, aiding in timely diagnosis and treatment.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Oral oncology
Background:
- Rising global incidence of lip and oral cavity cancer.
- Need for advanced early detection methods.
Purpose of the Study:
- To enhance early detection and classification of oral mucosal lesions using computer vision and deep learning.
- To evaluate YOLOv7 model performance for oral lesion triage.
Main Methods:
- Trained YOLOv7 deep learning models on a large dataset (>50,000 images) of oral mucosal lesions.
- Categorized lesions into benign, potentially malignant, and malignant grades.
- Compared performance of various YOLOv7 configurations, including YOLOv7-D6-CA.
Main Results:
- YOLOv7 models demonstrated high precision and recall for all lesion categories.
- YOLOv7-D6 model showed excellent performance in identifying malignant lesions.
- Coordinate attention integration (YOLOv7-D6-CA) improved classification accuracy.
Conclusions:
- YOLOv7 models are effective for classifying and triaging oral lesions.
- Deep learning offers valuable tools for early oral cancer detection in clinical and remote settings.
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