A comparative study of single-stage and dual-stage classification models for OPMDs

This study established a publicly available high-quality dataset of 1,348 oral mucosal disease images. Furthermore, it proposed a novel dual-stage classification model integrating Swin Transformer and DenseNet-169, which was demonstrated to outperform conventional single-stage classification model across key performance metrics such as accuracy and precision.

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Publication Details

Journal
BMC Oral Health
Published
2025-12-23
DOI
https://doi.org/10.1186/s12903-025-07582-y
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

A comparative study of single-stage and dual-stage classification models for OPMDs

W Liu, Jing Wen, Y H Li, Zhiwen Liu et al.
BMC Oral Health
AI in cancer detection
article

A comparative study of single-stage and dual-stage classification models for OPMDs

W Liu, Jing Wen, Y H Li, Zhiwen Liu, Jiayin Yu, Xuan Wang, Zihe Zhang, Rui Huang, Yiran Zhang, Qinwen Liu, Yuhang Ye, Weilan Su, Mei Ouyang
article en

Abstract

This study established a publicly available high-quality dataset of 1,348 oral mucosal disease images. Furthermore, it proposed a novel dual-stage classification model integrating Swin Transformer and DenseNet-169, which was demonstrated to outperform conventional single-stage classification model across key performance metrics such as accuracy and precision.

BMC Oral HealthVol. 26(1)
Central South University (CN), Hunan University of Traditional Chinese Medicine (CN), Shanghai University of Traditional Chinese Medicine (CN), Changsha Central Hospital (CN), Yueyang Hospital (CN), Jinan Stomatological Hospital (CN), Second Xiangya Hospital of Central South University (CN)
Openalex Percentile: Top 18%
AI in cancer detection
DOIOpen Access PDFOpenAlex
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A comparative study of single-stage and dual-stage classification models for OPMDs — W Liu, Jing Wen, et al. · BMC Oral Health (2025) | TGRS Research Map | TGRS