Ventricle Analysis in MR images for Neurodegenerative and Neuropsychiatric Disorder using Optimized Radiomic and Fused classification

Ventricle Analysis in MR images for Neurodegenerative and Neuropsychiatric Disorder using Optimized Radiomic and Fused classification | 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 Ventricle Analysis in MR images for Neurodegenerative and Neuropsychiatric Disorder using Optimized Radiomic and Fused classification Ahana Priyanka, Latha Manohar, Kavitha Ganesan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7386578/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 Dementia and schizophrenia are complex neurodisorders which require desired biomarkers to precisely differentiate between stages of severity. This work is an attempt to understand the brain left and right ventricle changes in MR images for normal, mild cognitive impairment, early MCI, MCI, late MCI, Alzheimer disease, Schizophrenia and Schizoaffective using significant radiomic features and fused classifiers. The images are obtained from public database. Then the sub-anatomic left and right ventricle regions are attained using Tsallis entropy-based thresholding and palindrome axis detection method. These segmented regions morphometric characterization is analysed with help of radiomic features. Further, significant features are selected from these regions. They are subject to SVM, KNN, NB, RF, LDA and fused classifier to differentiate the considered classes. Result suggests that left and right ventricle is prominently delineated with the average segmented accuracy of 96.4% and 97.2% respectively. The statistical test reveals that the prominent features from these regions show desired difference with p < 0.0001. Finally, the fused classifier is able to achieve the higher accuracy of 82.86% for left ventricle and 75.71% for right ventricle for considered classes. This concludes that left ventricle seems to relatively capable to distinguish the considered stages. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Neurology Biological sciences/Neuroscience Neurodisorder ventricle symmetry radiomic and fused classifier 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-7386578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531172390,"identity":"26a931c8-8cbd-434b-8921-176cd032a03b","order_by":0,"name":"Ahana 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classification","fulltext":[],"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":true,"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":"Neurodisorder, ventricle, symmetry, radiomic and fused classifier","lastPublishedDoi":"10.21203/rs.3.rs-7386578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7386578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDementia and schizophrenia are complex neurodisorders which require desired biomarkers to precisely differentiate between stages of severity. This work is an attempt to understand the brain left and right ventricle changes in MR images for normal, mild cognitive impairment, early MCI, MCI, late MCI, Alzheimer disease, Schizophrenia and Schizoaffective using significant radiomic features and fused classifiers. The images are obtained from public database. Then the sub-anatomic left and right ventricle regions are attained using Tsallis entropy-based thresholding and palindrome axis detection method. These segmented regions morphometric characterization is analysed with help of radiomic features. Further, significant features are selected from these regions. They are subject to SVM, KNN, NB, RF, LDA and fused classifier to differentiate the considered classes. Result suggests that left and right ventricle is prominently delineated with the average segmented accuracy of 96.4% and 97.2% respectively. The statistical test reveals that the prominent features from these regions show desired difference with p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. Finally, the fused classifier is able to achieve the higher accuracy of 82.86% for left ventricle and 75.71% for right ventricle for considered classes. 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