Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network
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 domi...
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| Veröffentlicht in: | International journal of environmental research and public health 2023-01, Vol.20 (3), p.2131 |
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| creator | Das, Madhusmita Dash, Rasmita Mishra, Sambit Kumar |
| description | 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. |
| doi_str_mv | 10.3390/ijerph20032131 |
| format | Article |
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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.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph20032131</identifier><identifier>PMID: 36767498</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Alcohol ; Algorithms ; Brain cancer ; Carcinoma, Squamous Cell - epidemiology ; Classification ; Datasets ; Decision trees ; Deep learning ; Head and Neck Neoplasms ; Human papillomavirus ; Humans ; Keratin ; Machine learning ; Medical imaging ; Medical research ; Morphology ; Mouth Mucosa ; Mouth Neoplasms - epidemiology ; Neural networks ; Neural Networks, Computer ; Oral cancer ; Oral carcinoma ; Oral squamous cell carcinoma ; Physicians ; Squamous cell carcinoma ; Squamous Cell Carcinoma of Head and Neck ; Support vector machines ; Tobacco</subject><ispartof>International journal of environmental research and public health, 2023-01, Vol.20 (3), p.2131</ispartof><rights>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://creativecommons.org/licenses/by/4.0/). 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Dash, Rasmita ; Mishra, Sambit Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3331-6cabcba9f86638947a1d92a14f8dd9cdda2261372f87791cbdbf8c0a32142d203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Alcohol</topic><topic>Algorithms</topic><topic>Brain cancer</topic><topic>Carcinoma, Squamous Cell - epidemiology</topic><topic>Classification</topic><topic>Datasets</topic><topic>Decision trees</topic><topic>Deep learning</topic><topic>Head and Neck Neoplasms</topic><topic>Human papillomavirus</topic><topic>Humans</topic><topic>Keratin</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Medical research</topic><topic>Morphology</topic><topic>Mouth Mucosa</topic><topic>Mouth Neoplasms - epidemiology</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Oral cancer</topic><topic>Oral carcinoma</topic><topic>Oral squamous cell carcinoma</topic><topic>Physicians</topic><topic>Squamous cell carcinoma</topic><topic>Squamous Cell Carcinoma of Head and Neck</topic><topic>Support vector machines</topic><topic>Tobacco</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Das, Madhusmita</creatorcontrib><creatorcontrib>Dash, Rasmita</creatorcontrib><creatorcontrib>Mishra, Sambit Kumar</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Das, Madhusmita</au><au>Dash, Rasmita</au><au>Mishra, Sambit Kumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2023-01-24</date><risdate>2023</risdate><volume>20</volume><issue>3</issue><spage>2131</spage><pages>2131-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>Worldwide, oral cancer is the sixth most common type of cancer. 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| subjects | Accuracy Alcohol Algorithms Brain cancer Carcinoma, Squamous Cell - epidemiology Classification Datasets Decision trees Deep learning Head and Neck Neoplasms Human papillomavirus Humans Keratin Machine learning Medical imaging Medical research Morphology Mouth Mucosa Mouth Neoplasms - epidemiology Neural networks Neural Networks, Computer Oral cancer Oral carcinoma Oral squamous cell carcinoma Physicians Squamous cell carcinoma Squamous Cell Carcinoma of Head and Neck Support vector machines Tobacco |
| title | Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network |
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