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Early lesions are often symptomless, while advanced disease may cause ulcers, lumps, bleeding, and related signs. The study proposes accurate automated identification and classification using deep learning, applying CNN and Transformer variants: a CANet model and a Swin-transformer-based approach. Experiments on the Kaggle Oral Cancer Images Dataset demonstrate high performance, supporting clinical assistive screening potential.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/intelligent-deep-learning-supports-biomedical-image-detection-and-classification-of-oral-cancer/330859/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/intelligent-deep-learning-supports-biomedical-image-detection-and-classification-of-oral-cancer/330859.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early identification of oral cancer important?","Question",{"text":112,"@type":113},"Early oral cancer is often asymptomatic, so timely identification can improve diagnosis, treatment decisions, and survival outcomes. Late-stage disease is harder to manage due to symptom progression.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What deep learning approaches does the paper use for detection and classification?",{"text":117,"@type":113},"The work uses two deep learning methods: CNN and Transformers. It proposes a CANet classification model with attention mechanisms and a classification model based on the Swin transformer.",{"name":119,"@type":110,"acceptedAnswer":120},"How well did the models perform on the Kaggle Oral Cancer Images Dataset?",{"text":121,"@type":113},"The Swin transformer achieved average accuracy 94.95%, sensitivity 95.37%, specificity 95.52%, and F1-Score 94.66%. The CANet model achieved average accuracy 97.00%, sensitivity 97.82%, specificity 97.82%, and F1-Score 96.61%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},330859,1790174342,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","Technology and Health Care 32 (2024) S465–S475 S465  \nDOI 10.3233/THC-248041 IOS Press  \nIntelligent deep learning supports biomedical image detection and classification of oral cancer  \nRongcan Chena ; 1 , Qinglian Wangb ; 1 and Xiaoyuan Huangc ;􀀃 a School of Informatics, Xiamen University, Xiamen, Fujian, China  \nb Department of Stomatology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China c Department of Stomatology, Zhongshan Hospital, Xiamen University, Xiamen, Fujian, China  \nAbstract.  \nBACKGROUND: 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.  \nOBJECTIVE: The objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer.  \nMETHODS: 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.  \nRESULTS: 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.  \nCONCLUSIONS: 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.  \nKeywords: Oral cancer classification, attention mechanism, transformer  \n1. Introduction  \nOral cancer is a cancer that occurs in the tissues of the mouth, usually originating in the mucosal cells lining the mouth. This cancer may involve different parts of the mouth, including the lips, floor of the  \n1Contributed equally to this work.  \n􀀃 Corresponding author: Xiaoyuan Huang, Department of Stomatology, Zhongshan Hospital, Xiamen University, Xiamen, [China. E-mail: huangxiaoyuan6531@163.com. ORCID:](China. E-mail: huangxiaoyuan6531@163.com. ORCID:) 0009-0006-2161-664X.  \n0928-7329 􀀍c 2024 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4 .0) .  \nS466 R. Chen et al. / Intelligent deep learning supports biomedical image detection and sclassification of oral cancer  \nmouth, tongue, buccal mucosa, gums, etc. Early and timely identification of OSCC is crucial to improve diagnosis, treatment, and survival. Commonly used detection methods are tissue biopsy and imaging examination. Diagnosis usually requires a tissue biopsy to check for abnormal cells in the affected area. X-rays, CT scans, and MRIs can help understand the size and spread of tumors. Despite our remarkable progress in understanding the molecular mechanisms of cancer, late-stage diagnosis remains a major constraint to the development of prec","cbCaivzFf7orMLGS","https://ap.wps.com/l/cbCaivzFf7orMLGS","pdf",1404306,11,"English","# Introduction\n## Background and clinical importance\n## Challenges of early detection\n## Role of deep learning and computer-aided diagnosis\n# Methods\n## CNN-based CANet\n## Transformer-based Swin approach\n# Results\n## Performance metrics and dataset evaluation\n# Conclusions","[{\"question\":\"Why is early identification of oral cancer important?\",\"answer\":\"Early oral cancer is often asymptomatic, so timely identification can improve diagnosis, treatment decisions, and survival outcomes. Late-stage disease is harder to manage due to symptom progression.\"},{\"question\":\"What deep learning approaches does the paper use for detection and classification?\",\"answer\":\"The work uses two deep learning methods: CNN and Transformers. It proposes a CANet classification model with attention mechanisms and a classification model based on the Swin transformer.\"},{\"question\":\"How well did the models perform on the Kaggle Oral Cancer Images Dataset?\",\"answer\":\"The Swin transformer achieved average accuracy 94.95%, sensitivity 95.37%, specificity 95.52%, and F1-Score 94.66%. The CANet model achieved average accuracy 97.00%, sensitivity 97.82%, specificity 97.82%, and F1-Score 96.61%.\"}]","Intelligent deep learning supports biomedical image detection and classification of oral cancer | PDF",1789990533,28]