Computer-aided Diagnosis System for Grading Brain Tumor Using Histopathology Images Based on Color and Texture Features

Computer-aided Diagnosis System for Grading Brain Tumor Using Histopathology Images Based on Color and Texture Features | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Computer-aided Diagnosis System for Grading Brain Tumor Using Histopathology Images Based on Color and Texture Features Naira Elazab, Wael GabAllah, Mohammed Elmogy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1847884/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). The purpose of this paper is to create an automated diagnostic system based on brain cancer histopathology images. In this paper, several imaging characteristics, including conventional intensity and advanced texturing features (grey co-occurrence, gray-level run-length matrix, and local binary pattern), were included in the training of a hybrid ensemble classification model. The textural and color characteristics were validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 94.6%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1847884","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":123968232,"identity":"d31cc1eb-1958-45b8-a4a1-cc305b99cf81","order_by":0,"name":"Naira Elazab","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Naira","middleName":"","lastName":"Elazab","suffix":""},{"id":123968233,"identity":"a46c665c-b289-42da-815a-715f238caeb7","order_by":1,"name":"Wael GabAllah","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wael","middleName":"","lastName":"GabAllah","suffix":""},{"id":123968234,"identity":"bc3d8f0c-3d20-4e98-82e5-22eb3b8ebf2a","order_by":2,"name":"Mohammed Elmogy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFAC5gYQKQfjEaOFEazFmHQtiQ1Ea9Ftb2zd8OHX4fS17WcPf2CosE5sYG+/gFeL2ZmDbTdn9h3O3XYmL02C4Ux6YgPPmQL8Wm4ktt3m7QFqOZBjxsDYdjixQSInAb+W+w/BWtLNzr8x/sD4D6hF/g0BLTcY227z/DicYHYjx0CCsQFkC/sBAn5JBPqlId1w2403ZhIJx9KN23hy8OpgMDt++NiND3+s5c3O5xh/+FBjLdvPfvwBfj0gwNjWDGGAPMHGwGNAWAvDnzpkHjsRtoyCUTAKRsFIAgD1JFPAr8uoawAAAABJRU5ErkJggg==","orcid":"","institution":"Mansoura University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Elmogy","suffix":""}],"badges":[],"createdAt":"2022-07-11 19:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1847884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1847884/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24604268,"identity":"6d9b8336-24ca-44da-b136-03b4b6598d7d","added_by":"auto","created_at":"2022-08-01 15:50:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":829835,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReportsM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1847884/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computer-aided Diagnosis System for Grading Brain Tumor Using Histopathology Images Based on Color and Texture Features","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1847884/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1847884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1847884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cancer pathology shows disease development and associated molecular features. 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