Neurojico-Driven Cognitive Image Classification Framework in Diagnosis Medical Imaging

Neurojico-Driven Cognitive Image Classification Framework in Diagnosis Medical Imaging | 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 Research Article Neurojico-Driven Cognitive Image Classification Framework in Diagnosis Medical Imaging Doaa Mohey Eldin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8992026/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 Neurojico-managed cognitive image classification is a new innovative framework to classifying cognitive medical imaging, utilizing whole-body perceptual attributes of attention extracted from images. This Neurojico cognitive classification framework is designed based on cognitive neuroscience, combined with advanced deep learning techniques, to improve diagnostic accuracy. It employs a novel image cognitive classification system to identify new perceptual patterns for four attributes: attention, recency, variety, and adaptability, rather than simply focusing on individual pixel blocks. Neurojico mimics how the human brain processes information, enabling it to understand medical images more clearly and clinically relevantly. The system utilizes Resnet50 and compares the perceptual images between a comparison of tailored convolutional neural networks (CNNs) and pre-trained Resnet50 for feature extraction then making a classification based on logistic regression. In addition, it converts the feature vector into cognitive weighted feature vector and applying the classification based on Logistic Regression Test Accuracy to achieve 98.6%. These results indicate that Neurojico outperforms traditional models, such as CNNs and transformer-based systems, in terms of accuracy, sensitivity, and the precision with which it interprets decisions. These maps help radiologists better understand the rationale behind the whole-body cognitive model`s decisions, making the system more transparent and reliable. Neurojico cognitive AI medical imaging cognitive classification neural dynamics semantic perception diagnostic reasoning Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8992026","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598393993,"identity":"d4fd9a70-498f-4666-bbd4-c7c8e1324bf2","order_by":0,"name":"Doaa Mohey Eldin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDCCAwwMzEBKjo29sfEBkMHDR6wWY36ew4cNQFrYiNWSOHNGWpoESICgFr4D3ImPCyoOM244kGNW+TXHToaNgfnhoxt4tEge4N1sPOPMYWaDA2fMbstuSwY6jM3YOAePFoMDvNukedsOsxkc7DG7LbmNGaiFh02asJZ/h3kMDvOYFUtuqydWS8NhCck2tjTGj9sOE9YieRjoF55j6Qb8PMyHpRm3HedhYybgF77jvRsf89RY17fJP2z8+HNbtT0/e/PDx/i0gCOFgaEZwuZBiBAEdWCS8QdxqkfBKBgFo2CEAQALPEd+XMwx0gAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Doaa","middleName":"Mohey","lastName":"Eldin","suffix":""}],"badges":[],"createdAt":"2026-02-28 02:15:13","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8992026/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8992026/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104400945,"identity":"ae168ccb-176d-42fd-95c1-5469c0c6aecc","added_by":"auto","created_at":"2026-03-11 12:11:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":749060,"visible":true,"origin":"","legend":"","description":"","filename":"paper3draft.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8992026/v1_covered_438dcb36-334f-45e1-9387-754fb62cd450.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNeurojico-Driven Cognitive Image Classification Framework in Diagnosis Medical Imaging\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Neurojico, cognitive AI, medical imaging, cognitive classification, neural dynamics, semantic perception, diagnostic reasoning","lastPublishedDoi":"10.21203/rs.3.rs-8992026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8992026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeurojico-managed cognitive image classification is a new innovative framework to classifying cognitive medical imaging, utilizing whole-body perceptual attributes of attention extracted from images. 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