{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T04:04:02Z","timestamp":1788321842831,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and technology special project, Institute of Wenzhou, Zhejiang University","award":["XMGL-KJZX-202401"],"award-info":[{"award-number":["XMGL-KJZX-202401"]}]},{"name":"Science and technology special project, Institute of Wenzhou, Zhejiang University","award":["LQ24F030003"],"award-info":[{"award-number":["LQ24F030003"]}]},{"name":"Science and technology special project, Institute of Wenzhou, Zhejiang University","award":["62303408"],"award-info":[{"award-number":["62303408"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["XMGL-KJZX-202401"],"award-info":[{"award-number":["XMGL-KJZX-202401"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ24F030003"],"award-info":[{"award-number":["LQ24F030003"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62303408"],"award-info":[{"award-number":["62303408"]}]},{"name":"National Natural Science Foundation of China","award":["XMGL-KJZX-202401"],"award-info":[{"award-number":["XMGL-KJZX-202401"]}]},{"name":"National Natural Science Foundation of China","award":["LQ24F030003"],"award-info":[{"award-number":["LQ24F030003"]}]},{"name":"National Natural Science Foundation of China","award":["62303408"],"award-info":[{"award-number":["62303408"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Given the complexity of oral mucosal disease diagnosis and the limitations in the precision of traditional object detection methods, this study aims to develop a high-accuracy artificial intelligence-assisted diagnostic approach based on the SegFormer semantic segmentation model. This method is designed to automatically segment lesion areas in white-light images of oral mucosal diseases, providing objective and quantifiable evidence for clinical diagnosis. This study utilized a dataset of oral mucosal diseases provided by the Affiliated Stomatological Hospital of Zhejiang University School of Medicine, comprising 838 high-resolution images of three diseases: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. These images were annotated at the pixel level by oral specialists using Labelme software (v5.5.0) to construct a semantic segmentation dataset. This study designed a SegFormer model based on the Transformer architecture, employed cross-validation to divide training and testing sets, and compared SegFormer models of different capacities with classical segmentation models such as UNet and DeepLabV3. Quantitative metrics including the Dice coefficient and mIoU were evaluated, and a qualitative visual analysis of the segmentation results was performed to comprehensively assess model performance. The SegFormer-B2 model achieved optimal performance on the test set, with a Dice coefficient of 0.710 and mIoU of 0.786, significantly outperforming other comparative algorithms. The visual results demonstrate that this model could accurately segment the lesion areas of three common oral mucosal diseases. The SegFormer model proposed in this study effectively achieves the precise automatic segmentation of three common oral mucosal diseases, providing a reliable auxiliary tool for clinical diagnosis. It shows promising prospects in improving the efficiency and accuracy of oral mucosal disease diagnosis and has potential clinical application value.<\/jats:p>","DOI":"10.3390\/bioengineering11111107","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T03:57:34Z","timestamp":1730692654000},"page":"1107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4805-1279","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China"},{"name":"Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310053, China"},{"name":"Life Health Innovation and Entrepreneurship Center, Institute of Wenzhou, Zhejiang University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310053, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9200-9541","authenticated-orcid":false,"given":"Fudong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310053, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Tian","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaowu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1822-7118","authenticated-orcid":false,"given":"Yuqi","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/odi.14516","article-title":"WHO\u2019s global oral health status report 2022: Actions, discussion and implementation","volume":"30","author":"Jain","year":"2024","journal-title":"Oral Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/s41572-020-00224-3","article-title":"Head and neck squamous cell carcinoma","volume":"6","author":"Johnson","year":"2020","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21654","article-title":"Cancer Statistics, 2021","volume":"71","author":"Siegel","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201327). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_10","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23\u201327). Swin-Unet: Unet-like pure transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101264","DOI":"10.1016\/j.jormas.2022.08.007","article-title":"A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study","volume":"124","author":"Keser","year":"2023","journal-title":"J. Stomatol. Oral Maxillofac. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, H., Chen, H., Weng, L., Shao, J., and Lin, J. (2021). Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis. J. Biomed. Opt., 26.","DOI":"10.1117\/1.JBO.26.8.086007"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"132677","DOI":"10.1109\/ACCESS.2020.3010180","article-title":"Automated detection and classification of oral lesions using deep learning for early detection of oral cancer","volume":"8","author":"Welikala","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1111\/jop.13227","article-title":"Automatic classification and detection of oral cancer in photographic images using deep learning algorithms","volume":"50","author":"Warin","year":"2021","journal-title":"J. Oral Pathol. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1055\/s-0042-1760300","article-title":"Artificial intelligence-based diagnosis of oral lichen planus using deep convolutional neural networks","volume":"17","author":"Achararit","year":"2023","journal-title":"Eur. J. Dent."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"SM, P.S., Shariff, M., Subramanyam, D.P., Varun, M.H., Shruthi, K., and Poornima, A.S. (2023, January 1\u20132). Real Time Oral Cavity Detection Leading to Oral Cancer using CNN. Proceedings of the 2023 International Conference on Network, Multimedia and Information Technology, Bengaluru, India.","DOI":"10.1109\/NMITCON58196.2023.10275851"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tanriver, G., Soluk Tekkesin, M., and Ergen, O. (2021). Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders. Cancers, 13.","DOI":"10.3390\/cancers13112766"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5318","DOI":"10.1364\/BOE.9.005318","article-title":"Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning","volume":"9","author":"Song","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1177\/00220345221089858","article-title":"Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning","volume":"101","author":"Yang","year":"2022","journal-title":"J. Dent. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"P, S.K., Lavanya, J., Kavya, G., Prasamya, N. (2022, January 16\u201318). Oral Cancer Diagnosis using Deep Learning for Early Detection. Proceedings of the 2022 International Conference on Electronics and Renewable Systems, Tuticorin, India.","DOI":"10.1109\/ICEARS53579.2022.9752280"},{"key":"ref_22","first-page":"6364102","article-title":"Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach","volume":"1","author":"Deif","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neunet.2020.05.003","article-title":"Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network","volume":"128","author":"Das","year":"2020","journal-title":"Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, D.W., Lee, S., Kwon, S., Nam, W., Cha, I., and Kim, H.J. (2019). Deep learning-based survival prediction of oral cancer patients. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-43372-7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","article-title":"Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm","volume":"145","author":"Jeyaraj","year":"2019","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106873","DOI":"10.1016\/j.oraloncology.2024.106873","article-title":"Utilizing deep learning for automated detection of oral lesions: A multicenter study","volume":"155","author":"Ye","year":"2024","journal-title":"Oral Oncol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105967","DOI":"10.1016\/j.oraloncology.2022.105967","article-title":"Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning","volume":"132","author":"Bagan","year":"2022","journal-title":"Oral Oncol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100558","DOI":"10.1016\/j.eclinm.2020.100558","article-title":"A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study","volume":"27","author":"Fu","year":"2020","journal-title":"eClinicalMedicine"},{"key":"ref_29","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1111\/odi.13054","article-title":"Role of Mammary Serine Protease Inhibitor on the Inflammatory Response in Oral Lichen Planus","volume":"25","author":"Xu","year":"2019","journal-title":"Oral Dis."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/11\/1107\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:27:07Z","timestamp":1760113627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/11\/1107"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,2]]},"references-count":32,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["bioengineering11111107"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11111107","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,2]]}}}