NDLI: Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning

Content Provider	Springer Nature : BioMed Central
Author	Lee, Sung-Jae Oh, Hyun Jun Son, Young-Don Kim, Jong-Hoon Kwon, Ik-Jae Kim, Bongju Lee, Jong-Ho Kim, Hang-Keun
Abstract	Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset. A total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network. Utilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative ${F}_{1}$ score improvement rate of OPMD was 4.9%. The present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.
Related Links	https://bmcoralhealth.biomedcentral.com/counter/pdf/10.1186/s12903-024-03898-3.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14726831
DOI	10.1186/s12903-024-03898-3
Journal	BMC Oral Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-02-01
Access Restriction	Open
Subject Keyword	Dentistry Oral and Maxillofacial Surgery Tongue cancer Class imbalance Deep learning Mosaic augmentation Curriculum learning
Content Type	Text
Resource Type	Article
Subject	Dentistry
Journal Impact Factor	2.6/2023
5-Year Journal Impact Factor	3.2/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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