[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-381366-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-381366-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","an-optimal-hierarchical-approach-for-oral-cancer-diagnosis-using-rough-set-theory-and-an-amended-version-of-the-competitive-search-algorithm","An Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm","","Oral cancer is characterized by uncontrolled cellular growth that damages nearby tissues, often starting as a persistent sore or lump in the mouth. If cancers of the cheeks, lips, floor of the mouth, tongue, sinuses, hard/soft palate, and lungs are not detected and treated early, they can be fatal. The study introduces a pipeline for diagnosing oral-cancer images: preprocessing and region-of-interest segmentation, feature extraction, feature selection via an amended competitive search optimizer, and final classification using a support vector machine. Evaluation on the Oral Cancer (Lips and Tongue) image (OCI) dataset shows performance supported by multiple indicators and comparisons with recent methods.",{"@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/an-optimal-hierarchical-approach-for-oral-cancer-diagnosis-using-rough-set-theory-and-an-amended-version-of-the-competitive-search-algorithm/381366/",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/an-optimal-hierarchical-approach-for-oral-cancer-diagnosis-using-rough-set-theory-and-an-amended-version-of-the-competitive-search-algorithm/381366.png","ImageObject",300,407,{"name":42,"@type":43},"Sophia Brooks","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of the proposed study?","Question",{"text":63,"@type":64},"To develop an efficient diagnostic system pipeline for oral cancer images by selecting useful features and classifying them with an optimized model.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the method process the oral cancer images?",{"text":68,"@type":64},"It preprocesses images, segments the region of interest, extracts characteristics, selects useful features, and removes others to reduce complexity.",{"name":70,"@type":61,"acceptedAnswer":71},"Which models and optimization technique are used for classification and feature selection?",{"text":72,"@type":64},"Feature selection is optimized using an amended version of the competitive search optimizer, and the final classification is performed with a support vector machine (SVM).","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},381366,1790304881,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,126],{"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":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"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},962084925636,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","diagnostics  \nArticle  \nAn Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm  \nSimin Song 1,†, Xiaojing Ren 2,†, Jing He 1, Meng Gao 1, Jia'nan Wang 1 and Bin Wang 1, *  \nCitation: Song, S.; Ren, X.; He, J.; Gao, M.; Wang, J.; Wang, B. An Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm. Diagnostics 2023, 13, 2454 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics13142454  \n1 The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China  \n2 The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100853, China; [xiaojingren@gmail.com](xiaojingren@gmail.com)  \n* Correspondence: [liuziqing301@163.com](liuziqing301@163.com)  \n† These authors contributed equally to this work.  \nAbstract: Oral cancer is introduced as the uncontrolled cells' growth that causes destruction and damage to nearby tissues. This occurs when a sore or lump grows in the mouth that does not disappear. Cancers of the cheeks, lips, floor of the mouth, tongue, sinuses, hard and soft palate, and lungs (throat) are types of this cancer that will be deadly if not detected and cured in the beginning stages. The present study proposes a new pipeline procedure for providing an efficient diagnosis system for oral cancer images. In this procedure, after preprocessing and segmenting the area of interest of the inputted images, the useful characteristics are achieved. Then, some number of useful features are selected, and the others are removed to simplify the method complexity. Finally, the selected features move into a support vector machine (SVM) to classify the images by selected characteristics. The feature selection and classification steps are optimized by an amended version of the competitive search optimizer. The technique is finally implemented on the Oral Cancer (Lips and Tongue) images (OCI) dataset, and its achievements are confirmed by the comparison of it with some other latest techniques, which are weight balancing, a support vector machine, a gray-level co-occurrence matrix (GLCM), the deep method, transfer learning, mobile microscopy, and quadratic discriminant analysis. The simulation results were authenticated by four indicators and indicated the suggested method's efficiency in relation to the others in diagnosing the oral cancer cases.  \nKeywords: diagnosis; oral cancer; rough set theory; support vector machine; K-means; amended competitive search algorithm  \n1. Introduction  \nAcademic Editor: Gianna Dipalma  \nReceived: 3 May 2023  \nRevised: 6 July 2023  \nAccepted: 19 July 2023  \nPublished: 24 July 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/)) .  \nOne of the most common cancers is oral cancer, and many factors are involved in the development of this cancer. These proliferating cells accumulate together and form cancerous masses, which sometimes invade other parts of the body and cause problems that can be dangerous to people. Oral cancer is one of these cancers that can be dangerous for people if it is not diagnosed in time. Cancers of the cheeks, lips, floor of the mouth, tongue, sinuses, hard and soft palate, and lungs (throat) are types of this cancer that will be deadly if not detected and cured in the beginning stages [1] . Oral cancer can appear on the lips or other parts of the mouth with the tissues inside the gums, lips, and tongue. Oral cancer often changes parts of the skin [2] . For example, it causes the growth of thick tissue or the appearance of wou","cbCaiiVRAtNYJuqr","https://ap.wps.com/l/cbCaiiVRAtNYJuqr","pdf",4024651,21,"English","# Introduction\n## Problem background and clinical relevance\n## Motivation for AI-assisted oral cancer diagnosis\n## Proposed method overview","[{\"question\":\"What is the main goal of the proposed study?\",\"answer\":\"To develop an efficient diagnostic system pipeline for oral cancer images by selecting useful features and classifying them with an optimized model.\"},{\"question\":\"How does the method process the oral cancer images?\",\"answer\":\"It preprocesses images, segments the region of interest, extracts characteristics, selects useful features, and removes others to reduce complexity.\"},{\"question\":\"Which models and optimization technique are used for classification and feature selection?\",\"answer\":\"Feature selection is optimized using an amended version of the competitive search optimizer, and the final classification is performed with a support vector machine (SVM).\"}]","An Optimal Hierarchical Approach for Oral Cancer Diagnosis Using Rough Set Theory and an Amended Version of the Competitive Search Algorithm | PDF",1790245029,53]