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This study proposes an automatic OSCC detection approach using a convolutional neural network and evaluates histopathological oral cancer images. The model is compared with VGG16, VGG19, AlexNet, ResNet50/101, MobileNet, and Inception architectures.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/automatic-detection-of-oral-squamous-cell-carcinoma-from-histopathological-images-of-oral-mucosa-using-deep-convolutional-neural-network/382087/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/automatic-detection-of-oral-squamous-cell-carcinoma-from-histopathological-images-of-oral-mucosa-using-deep-convolutional-neural-network/382087.png","ImageObject",300,407,{"name":42,"@type":43},"anakgang17","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-25","2026-09-24",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is early detection of OSCC important?","Question",{"text":63,"@type":64},"Early detection of OSCC in its preliminary stage increases chances for better treatment and proper therapy.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What method does the study propose for OSCC detection?",{"text":68,"@type":64},"It proposes a convolutional neural network model for automatic and early detection of OSCC using histopathological oral cancer images.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the proposed model compare with other deep learning models?",{"text":72,"@type":64},"The model is compared with VGG16, VGG19, AlexNet, ResNet50, ResNet101, MobileNet, and Inception architectures.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},382087,1790754865,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},962090883568,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Article  \nAutomatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network  \nMadhusmita Das 1, Rasmita Dash 2, * and Sambit Kumar Mishra 3, *  \nCitation: Das, M.; Dash, R.; Mishra, S.K. Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network. Int. J. Environ. Res. Public Health 2023, 20, 2131. [https://](https://)[ ](https://)[doi.org/10.3390/ijerph20032131](doi.org/10.3390/ijerph20032131)  \nAcademic Editor: Kenneth Lee Sutherland  \nReceived: 8 December 2022  \nRevised: 19 January 2023  \nAccepted: 20 January 2023  \nPublished: 24 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Application, Siksha `O' Anusandhan Deemed to be University, Bhubaneswar 751030, India  \n2 Department of Computer Science and Engineering, Siksha `O' Anusandhan Deemed to be University, Bhubaneswar 751030, India  \n3 Department of Computer Science and Engineering, SRM University-AP, Guntur 522240, India  \n* Correspondence: [rasmitadash@soa.ac.in](rasmitadash@soa.ac.in) (R.D.); [skmishra.nitrkl@gmail.com](skmishra.nitrkl@gmail.com) (S.K.M.)  \nAbstract: Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC) . The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data.  \nKeywords: deep convolutional neural network; histopathological images; oral cancer  \n1. Introduction  \nIn India, the rate of oral cancer, specifically OSCC, is increasing, and the main reason behind this rise is alcohol consumption and tobacco chewing, etc. The death rate of oral cancer is also high in India. There are commercial advertisements regarding the disadvantage of tobacco and alcohol consumption; however, due to the lack of understanding and inadequate knowledge, people are still in the habit of alcohol and tobacco, which causes an increase in the number of oral cancer patients [1,2] . One of the international agencies for research conducted a survey and predicted that the number of cancer patients will increase from 1 million in 2012 to more than 1.7 million by 2035 . This implies the death rate will also increase from 680,000 to 1–2 million by 2035 [3] . Hence, it is utmost important to detect the OSCC at its early stage so that the treatment can be started as early as possible and the death rate due to oral cancer can be reduced. With the advancement of technology, continuous research has been performed by many researchers for the early detection of oral cancer and, at the same time, enormous amounts of oral cance","cbCaib5CtLdmhMag","https://ap.wps.com/l/cbCaib5CtLdmhMag","pdf",5479998,21,"English","# Introduction\n## Problem context and rationale\n## Related screening and diagnostic approaches\n## Role of histopathological imaging\n## Feature extraction and machine learning background\n## Transition to convolutional neural networks (CNN)","[{\"question\":\"Why is early detection of OSCC important?\",\"answer\":\"Early detection of OSCC in its preliminary stage increases chances for better treatment and proper therapy.\"},{\"question\":\"What method does the study propose for OSCC detection?\",\"answer\":\"It proposes a convolutional neural network model for automatic and early detection of OSCC using histopathological oral cancer images.\"},{\"question\":\"How does the proposed model compare with other deep learning models?\",\"answer\":\"The model is compared with VGG16, VGG19, AlexNet, ResNet50, ResNet101, MobileNet, and Inception architectures.\"}]","Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network | PDF",1790248954,53]