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This study proposes an image-processing and deep-learning framework that integrates an improved tunicate swarm algorithm to enhance a convolutional neural network. The method is evaluated on an oral cancer image dataset and compared with published approaches using multiple metrics. Results show 98.70% accuracy, 93.71% recall, 96.42% precision, and 90.08% F1-score, supporting use alongside clinicians.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/convolutional-neural-network-for-oral-cancer-detection-combined-with-improved-tunicate-swarm-algorithm-to-detect-oral-cancer/329450/",{"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/convolutional-neural-network-for-oral-cancer-detection-combined-with-improved-tunicate-swarm-algorithm-to-detect-oral-cancer/329450.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early diagnosis of oral cancer important?","Question",{"text":112,"@type":113},"Early diagnosis can save patients from some oral malignancies and enables timely treatment, improving the chance of curing or controlling complications.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What does the proposed method combine to detect oral cancer?",{"text":117,"@type":113},"The approach integrates image processing and deep learning with a convolutional neural network improved using an improved tunicate swarm algorithm.",{"name":119,"@type":110,"acceptedAnswer":120},"How effective is the proposed model based on the reported metrics?",{"text":121,"@type":113},"The model achieves 98.70% accuracy, 93.71% recall, 96.42% precision, and 90.08% F1-score for detecting oral cancerous lesions from photographic images.","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},329450,1792193705,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"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,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nConvolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer  \nXiao Wei1, Liu Chanjuan2, Jiang Ke2, Ye Linyun1, Gao Jinxing1􀀍 & Wang Quanbing1􀀍  \nEarly Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing of patients and the preservation of their  \nlives, there are several causes for poor and wrong diagnosis of oral cancer. In recent years, the use of computer-aided design diagnosis tools as an auxiliary tool alongside clinicians has greatly benefited in more accurate identification of this malignancy. The current study proposes a new approach for identifying oral cancer patients based on image processing and deep learning. The current study employs a recently integrated model of an improved tunicate swarm algorithm to produce an efficient tool for improving a convolutional neural network and delivering an accurate cancer diagnostic system. The approach is then implemented on the oral cancer pictures dataset. The approach is then validated by comparing it to other published papers using various measurement markers. The proposed model achieved an accuracy of 98.70% and a recall of 93.71% in detecting oral cancerous lesions from photographic images. The model also achieved an F1-score of 90.08% and a precision of 96.42%. The final results demonstrate that the offered approach can produce more exact results and can be used in conjunction with clinicians to help in diagnosing oral cancer.  \nKeywords Oral cancer detection, Convolutional neural networks, Improved tunicate swarm algorithm, Photographic image analysis, Deep learning  \nBackground  \nOral cancer is the beginning of the unusual growth of some cells in the mouth and the formation of a mass or ulcer1. One of the most prevalent kinds of cancer in South and Central Asia is oral cancer2. Based on the American Cancer Society3, there exist around 54,000 novel samples of oral cancer or oropharyngeal per year. Also, 11,230 individuals die because of oral cancer or oropharyngeal.  \nCommonly, pre-oral lesions of oral cancer include Erythroplakia, Oral sub-mucous fibrosis (OSF), leukoplakia, and so on. OSF is a progressive and chronic cheater, and the circumstance before it is cancer with an extreme degree of malignant potential. The high quantity of this case turns into oral cancer (OC) . The precancerous condition can be diagnosed by the characteristics of the microscopic4. These features are obtained from oral mucosal disintegration (OED) or cellular tissue that possesses various degrees following the involved points in the mucosal area5. In addition, related disintegration is excessive mucosal cell proliferation, or in rare cases, it shows mucosal thinning. If mucosal thinning is not visible in the advanced stage, excessive proliferation of cells or the thinning stage may cause changes in the architecture and tissue of the lining cells to cause nemydra.  \nA cancerous lump in the mouth cannot be treated or removed naturally. Contrary to popular belief, this cancer does not only mean the beginning and growth of complications in one area, this disease can start and develop anywhere in the oral cavity.  \nOral cancer means a lump or sore that is formed in the mouth and does not go away. In general, this cancer can influence distinct regions of the oral cavity such as the cheeks, floor of the mouth, sinuses, and pharynx. In general, it cannot be said that this cancer is fatal6. Because all types of cancer will have more chances of treatment with early detection. In other words, this cancer can be cured if it is diagnosed in time and effective treatment is started.  \n1Zhejiang Provincial JianDe First People’s Hospital, HangZhou, Zhejiang, China. 2graduate school, Bengbu Medical College, Bengbu, AnHui, China. 􀀍 email: [gaojinxing1978@163.com](gaojinxing1978","cbCaiaw3ncyHX4p6","https://ap.wps.com/l/cbCaiaw3ncyHX4p6","pdf",2857464,17,"English","# Background\n## Oral cancer description and clinical importance\n## Common pre-oral lesions and precancerous diagnosis\n## Current diagnostic approaches and limitations\n## Study objective and proposed approach","[{\"question\":\"Why is early diagnosis of oral cancer important?\",\"answer\":\"Early diagnosis can save patients from some oral malignancies and enables timely treatment, improving the chance of curing or controlling complications.\"},{\"question\":\"What does the proposed method combine to detect oral cancer?\",\"answer\":\"The approach integrates image processing and deep learning with a convolutional neural network improved using an improved tunicate swarm algorithm.\"},{\"question\":\"How effective is the proposed model based on the reported metrics?\",\"answer\":\"The model achieves 98.70% accuracy, 93.71% recall, 96.42% precision, and 90.08% F1-score for detecting oral cancerous lesions from photographic images.\"}]","Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer | PDF",1789984171,43]