{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T09:51:18Z","timestamp":1791625878336,"version":"4.3.4"},"reference-count":25,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>BACKGROUND: Oral cancer is a malignant tumor that usually occurs within the tissues of the mouth. This type of cancer mainly includes tumors in the lining of the mouth, tongue, lips, buccal mucosa and gums. Oral cancer is on the rise globally, especially in some specific risk groups. The early stage of oral cancer is usually asymptomatic, while the late stage may present with ulcers, lumps, bleeding, etc. OBJECTIVE: The objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer. METHODS: We applied two deep learning methods, CNN and Transformers. First, we propose a new CANet classification model for oral cancer, which uses attention mechanisms combined with neglected location information to explore the complex combination of attention mechanisms and deep networks, and fully tap the potential of attention mechanisms. Secondly, we design a classification model based on Swim transform. The image is segmented into a series of two-dimensional image blocks, which are then processed by multiple layers of conversion blocks. RESULTS: The proposed classification model was trained and predicted on Kaggle Oral Cancer Images Dataset, and satisfactory results were obtained. The average accuracy, sensitivity, specificity and F1-Socre of Swin transformer architecture are 94.95%, 95.37%, 95.52% and 94.66%, respectively. The average accuracy, sensitivity, specificity and F1-Score of CANet model were 97.00%, 97.82%, 97.82% and 96.61%, respectively. CONCLUSIONS: We studied different deep learning algorithms for oral cancer classification, including convolutional neural networks, converters, etc. Our Attention module in CANet leverages the benefits of channel attention to model the relationships between channels while encoding precise location information that captures the long-term dependencies of the network. The model achieves a high classification effect with an accuracy of 97.00%, which can be used in the automatic recognition and classification of oral cancer.<\/jats:p>","DOI":"10.3233\/thc-248041","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T11:02:52Z","timestamp":1715684572000},"page":"465-475","source":"Crossref","is-referenced-by-count":21,"title":["Intelligent deep learning supports biomedical image detection and classification of oral cancer"],"prefix":"10.1177","volume":"32","author":[{"given":"Rongcan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, Fujian, China"},{"name":"School of Informatics, Xiamen University, Xiamen, Fujian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Stomatology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China"},{"name":"School of Informatics, Xiamen University, Xiamen, Fujian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Stomatology, Zhongshan Hospital, Xiamen University, Xiamen, Fujian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/THC-248041_ref1","first-page":"187","article-title":"Computer intelligence in detection of malignant or premalignant oral lesions: the story so far","author":"Singh","year":"2022","journal-title":"Computational Intelligence in Oncology: Applications in Diagnosis. Prognosis and Therapeutics of Cancers."},{"key":"10.3233\/THC-248041_ref2","doi-asserted-by":"crossref","first-page":"146478","DOI":"10.1109\/ACCESS.2021.3123472","article-title":"IoMT cloud-based intelligent prediction of breast cancer stages empowered with deep learning","volume":"9","author":"Siddiqui","year":"2021","journal-title":"IEEE Access."},{"issue":"3","key":"10.3233\/THC-248041_ref3","doi-asserted-by":"crossref","first-page":"e12764","DOI":"10.1111\/exsy.12764","article-title":"Optimal deep learning based fusion model for biomedical image classification","volume":"39","author":"Mansour","year":"2022","journal-title":"Expert Systems."},{"key":"10.3233\/THC-248041_ref4","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s13187-017-1275-7","article-title":"Population survey of knowledge about oral cancer and related factors in the capital of Iran","volume":"34","author":"Azimi","year":"2019","journal-title":"Journal of Cancer Education."},{"issue":"1","key":"10.3233\/THC-248041_ref5","doi-asserted-by":"crossref","first-page":"015001","DOI":"10.1117\/1.JBO.27.1.015001","article-title":"Interpretable deep learning approach for oral cancer classification using guided attention inference network","volume":"27","author":"Figueroa","year":"2022","journal-title":"Journal of Biomedical Optics."},{"key":"10.3233\/THC-248041_ref6","doi-asserted-by":"crossref","unstructured":"Lim JH, Tan CS, Chan CS, et al. D\u2019OraCa: deep learning-based classification of oral lesions with mouth landmark guidance for early detection of oral cancer\/\/Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12\u201314, 2021, Proceedings 25. Springer International Publishing. 2021: 408-422.","DOI":"10.1007\/978-3-030-80432-9_31"},{"issue":"1","key":"10.3233\/THC-248041_ref7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1093\/comjnl\/bxaa136","article-title":"Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer","volume":"65","author":"Shamim","year":"2022","journal-title":"The Computer Journal."},{"issue":"4","key":"10.3233\/THC-248041_ref8","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1109\/TBCAS.2019.2918244","article-title":"Texture-map-based branch-collaborative network for oral cancer detection","volume":"13","author":"Chan","year":"2019","journal-title":"IEEE Transactions on Biomedical Circuits and Systems."},{"issue":"1","key":"10.3233\/THC-248041_ref9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"10.3233\/THC-248041_ref10","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s41095-021-0247-3","article-title":"Transformers in computational visual media: A survey","volume":"8","author":"Xu","year":"2022","journal-title":"Computational Visual Media."},{"key":"10.3233\/THC-248041_ref11","doi-asserted-by":"crossref","first-page":"27867","DOI":"10.1007\/s11042-020-09384-6","article-title":"Deep learning neural network for texture feature extraction in oral cancer: Enhanced loss function","volume":"79","author":"Bhandari","year":"2020","journal-title":"Multimedia Tools and Applications."},{"key":"10.3233\/THC-248041_ref12","doi-asserted-by":"crossref","unstructured":"Lu J, Sladoje N, Runow Stark C, et al. A deep learning based pipeline for efficient oral cancer screening on whole slide images\/\/International Conference on Image Analysis and Recognition. Cham: Springer International Publishing. 2020: 249-261.","DOI":"10.1007\/978-3-030-50516-5_22"},{"issue":"11","key":"10.3233\/THC-248041_ref13","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":"Biomedical Optics Express."},{"key":"10.3233\/THC-248041_ref14","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017: 30."},{"key":"10.3233\/THC-248041_ref15","doi-asserted-by":"crossref","unstructured":"Gheflati B, Rivaz H. Vision transformers for classification of breast ultrasound images\/\/2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE. 2022: 480-483.","DOI":"10.1109\/EMBC48229.2022.9871809"},{"issue":"2","key":"10.3233\/THC-248041_ref16","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3390\/info11020125","article-title":"Albumentations: fast and flexible image augmentations","volume":"11","author":"Buslaev","year":"2020","journal-title":"Information."},{"key":"10.3233\/THC-248041_ref17","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention\/\/ International conference on machine learning","author":"Touvron","year":"2021","journal-title":"PMLR."},{"key":"10.3233\/THC-248041_ref18","doi-asserted-by":"crossref","unstructured":"Yuan L, Chen Y, Wang T, et al. Tokens-to-token vit: Training vision transformers from scratch on imagenet\/\/Proceedings of the IEEE\/CVF international conference on computer vision. 2021: 558-567.","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"10.3233\/THC-248041_ref19","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, et al. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions\/\/Proceedings of the IEEE\/CVF international conference on computer vision. 2021: 568-578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.3233\/THC-248041_ref20","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows\/\/Proceedings of the IEEE\/CVF international conference on computer vision. 2021: 10012-10022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.3233\/THC-248041_ref21","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021: 13713-13722.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"10.3233\/THC-248041_ref22","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2921-2929.","DOI":"10.1109\/CVPR.2016.319"},{"key":"10.3233\/THC-248041_ref23","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv preprint arXiv2010.11929, 2020."},{"issue":"1","key":"10.3233\/THC-248041_ref24","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3390\/healthcare11010113","article-title":"Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment","volume":"11","author":"Alabdan","year":"2022","journal-title":"Healthcare. MDPI."},{"key":"10.3233\/THC-248041_ref25","first-page":"2022","article-title":"Intelligent deep learning enabled oral squamous cell carcinoma detection and classification using biomedical images","author":"Alanazi","year":"2022","journal-title":"Computational Intelligence and Neuroscience."}],"container-title":["Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/THC-248041","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:21:03Z","timestamp":1777612863000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/THC-248041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":25,"URL":"https:\/\/doi.org\/10.3233\/thc-248041","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,31]]}}}