Publication: Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images
1
0
Issued Date
2025-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105013858871
Journal Title
Scientific Reports
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.15 No.1 (2025)
Suggested Citation
Khovidhunkit S.O.P., Phosri K., Thanathornwong B., Rungraungrayabkul D., Poomrittigul S., Treebupachatsakul T. Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images. Scientific Reports Vol.15 No.1 (2025). doi:10.1038/s41598-025-14450-w Retrieved from: https://hdl.handle.net/20.500.14740/50360
Corresponding Author(s)
Other Contributor(s)
Abstract
Computer vision adjunctive technology for oral lesion diagnoses has been developed to detect and identify Oral Potentially Malignant Disorders (OPMDs) and non-OPMDs. The early detection of OPMDs can reduce the risk of oral cancer development, improving the survival rate of the patients. This study aims to evaluate the computer vision technique in the white oral lesion domain within the scope of photographic images. Deep learning techniques for the classification of Convolution Neural Networks (CNNs) and transformer neural networks, and one-stage models of YOLOv7 and YOLOv8 were utilized to classify and detect five classes of OPMDs and non-OPMDs oral white lesions including oral leukoplakia, oral lichen planus, pseudomembranous candidiasis, oral ulcers covered with pseudomembrane and other white benign oral lesions. From the evaluation results of classification, the IFormerBase model achieves overperformance compared to CNN models with accuracy, precision, and F1 score of more than 80% on the test set. The best model for object detection is YOLOv7 with 84.5% mean Average Precision (mAP) at Intersection over Union (IoU) threshold of 0.3 and 74.5% at IoU of 0.5 on the test set. Object detection results reveal promising automatic oral lesion identification, which can be further developed to enhance the lesion screening system.