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From September 2019 to October 2023, 205 high-quality annotated oral cancer images were collected at the Chinese PLA General Hospital. U-Net and ResNet-34 deep learning models were trained and evaluated with Dice coefficient, IoU, loss, precision, recall, and F1 score. Results show Dice around 0.8 in training and up to 0.96 precision and 0.58 F1 in testing. The study demonstrates the potential of deep learning for early identification.",{"@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/development-of-an-oral-cancer-detection-system-through-deep-learning-paper-abstract/331599/",{"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/development-of-an-oral-cancer-detection-system-through-deep-learning-paper-abstract/331599.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study’s main objective?","Question",{"text":112,"@type":113},"To develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images and detect oral cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the data and models handled in the study?",{"text":117,"@type":113},"Annotated oral cancer images were collected from September 2019 to October 2023, and U-Net and ResNet-34 models were employed for detection.",{"name":119,"@type":110,"acceptedAnswer":120},"Which evaluation metrics were used for performance assessment?",{"text":121,"@type":113},"Dice coefficient, Intersection over Union (IoU), loss, precision, recall, and F1 score were used to evaluate the models.","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},331599,1790417613,{"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":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","Li etal. BMCOral Health (2024) 24:1468 [https://doi.org/10.1186/s12903-024-05195-5](https://doi.org/10.1186/s12903-024-05195-5)  \nBMC Oral Health  \nRESEARCH Open Access  \nDevelopment of an oral cancer detection system through deep learning  \nLiangbo Li 1,2†, Cheng Pu3,4†, Jingqiao Tao 1,5†, Liang Zhu 1,2, Suixin Hu2, Bo Qiao2, Lejun Xing2, Bo Wei2, Chuyan Shi2, Peng Chen2* and Haizhong Zhang2*  \nAbstract  \nObjective We aimed to develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images of patients for the detection of oral cancer.  \nSubjects and methods From September 2019 to October 2023, 205 high-quality annotated images of oral cancer were collected using a portable oral electronic endoscope at the Chinese PLA General Hospital for this study. The U-Net and ResNet-34 deep learning models were employed for oral cancer detection. The performance of these models was evaluated using several metrics: Dice coefficient, Intersection over Union (IoU), Loss, Precision, Recall, and F1 Score.  \nResults During the algorithm model training phase, the Dice values were approximately 0 . 8, the Loss values were close to 0, and the IoU values were around 0.7. In the validation phase, the highest Dice values ranged between 0.4 and 0 . 5, while the Loss values increased, and the training loss began to decrease gradually. In the test phase, the model achieved a maximum Precision of 0.96 with a confidence threshold of 0.990. Additionally, with a confidence threshold of 0 . 010, the highest F1 score reached was 0.58.  \nConclusion This study provides an initial demonstration of the potential of deep learning models in identifying oral cancer.  \nKeywords Artificial intelligence, Deep learning, Oral cancer  \n†Liangbo Li, Cheng Pu and Jingqiao Tao Contributed equally .  \n*Correspondence:  \nPeng Chen[chenpeng@301hospital.com.cn](chenpeng@301hospital.com.cn)[ ](chenpeng@301hospital.com.cn)Haizhong Zhang [zhanghaizhong@301hospital.com.cn](zhanghaizhong@301hospital.com.cn)  \n1Medical School of Chinese PLA, Beijing, China  \n2Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing road, Haidian District, Beijing 100853, China  \n3Key Laboratory of Animal Disease and Human Health of Sichuan Province, Beijing, China  \n4College of Veterinary Medicine, Sichuan Agricultural University, Sichuan, China  \n5Department of stomatology , Southern Medical Branch of PLA General Hospital, Beijing 100842, China  \nIntroduction  \nOral cancer ranks 18th among 36 major cancers worldwide, with an estimated 377,731 new diagnoses and over 177,757 deaths in 2020 [1]. According to the latest epidemiological survey data released by the American Cancer Society in 2023, there are 54,540 new cases of oral cancer and 7,400 deaths annually in the United States [2]. The 5-year survival rate for U.S. patients diagnosed with localized disease is 83%, compared to only 32% for patients with metastatic cancer [3]. More than two-thirds of oral cancers are detected after distant metastasis, marking the highest incidence and mortality rates among major cancers [4]. Therefore, early detection is essential to ensure optimal outcomes.  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permi","cbCaidpQ5T8XYD7l","https://ap.wps.com/l/cbCaidpQ5T8XYD7l","pdf",1365232,"English","# Abstract\n# Introduction\n## Burden of oral cancer and need for early detection\n## Limitations of traditional visible-light screening\n## Role of artificial intelligence and computer vision","[{\"question\":\"What is the study’s main objective?\",\"answer\":\"To develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images and detect oral cancer.\"},{\"question\":\"How were the data and models handled in the study?\",\"answer\":\"Annotated oral cancer images were collected from September 2019 to October 2023, and U-Net and ResNet-34 models were employed for detection.\"},{\"question\":\"Which evaluation metrics were used for performance assessment?\",\"answer\":\"Dice coefficient, Intersection over Union (IoU), loss, precision, recall, and F1 score were used to evaluate the models.\"}]","Development of an oral cancer detection system through deep learning - Paper abstract | PDF",1789994253,25]