NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning | 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 NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning Sonia Arora, Gouri Sankar Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4026454/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 9 You are reading this latest preprint version Abstract This study presents a robust framework for the classification of brain tumors, beginning with meticulous data curation from 233 patients. The dataset comprises a diverse range of T1-weighted contrast-enhanced images, encompassing meningioma, glioma, and pituitary tumor types. Rigorous organization, pre-processing, and augmentation techniques are applied to optimize model training. The proposed self-adaptive model incorporates a cutting-edge algorithm, leveraging Adaptive Contrast Limited Histogram Equalization (CLAHE) and Self-Adaptive Spatial Attention. CLAHE enhances grayscale images by tailoring contrast to the unique characteristics of each region. The Self-Adaptive Spatial Attention, implemented through an Attention Layer, dynamically assigns weights to spatial locations, thereby enhancing sensitivity to critical brain regions. The model architecture integrates transfer learning models, including DenseNet169, DenseNet201, ResNet152, and InceptionResNetV2, contributing to its robustness. DenseNet169 serves as a feature extractor, capturing hierarchical features through pre-trained weights. Model adaptability is further enriched by components such as batch normalization, dropout, layer normalization, and an adaptive learning rate strategy, mitigating overfitting and dynamically adjusting learning rates during training. Technical details, including the use of the Adam optimizer and softmax activation function, underscore the model's optimization and multi-class classification capabilities. The proposed model, which amalgamates transfer learning and adaptive mechanisms, emerges as a powerful tool for brain tumor detection and classification in medical imaging. Its nuanced comprehension of brain tumor images, facilitated by self-adaptive attention mechanisms, positions it as a promising advancement in computer-aided diagnosis in neuroimaging. Leveraging DenseNet201 with a self-adaptive mechanism, the model surpasses previous methods, achieving an accuracy of 94.85%, precision of 95.16%, and recall of 94.60%, showcasing its potential for enhanced accuracy and generalization in the challenging realm of medical image analysis. NeuroInsight Brain Tumor Classification Medical Imaging Adaptive Deep Learning Self-Adaptive Framework Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 13 May, 2024 Reviews received at journal 06 May, 2024 Reviewers agreed at journal 09 Apr, 2024 Reviews received at journal 21 Mar, 2024 Reviewers agreed at journal 14 Mar, 2024 Reviewers invited by journal 14 Mar, 2024 Editor assigned by journal 08 Mar, 2024 Submission checks completed at journal 08 Mar, 2024 First submitted to journal 07 Mar, 2024 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. 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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-4026454","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277443170,"identity":"d07e6a09-262f-4e84-95ef-8ec407e770bb","order_by":0,"name":"Sonia Arora","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYJCCA0CcwHC8sfEBkMHDR7yWM4ebDUBa2Ii1KYHhRnqbBIhFUIt8e+/BAz8Y7uTxHUhsq/yaYyfDxsD88NENPFoMzpxLONjD8KxY8sDBttuy25KBDmMzNs7Bp0Uix+AAD8PhxA0HG9tuS25jBmrhYZPGp0V+Ro7BwT8gLYcZ24olt9UT1sJwI8fgMNiWY4xtjB+3HSasxeDMGYPDMgbPEmeeYWyWZtx2nIeNmYBf5Nt7jD++qbiT2Hf/+cOPP7dV2/OzNz98jNdhELsOgClmHjBJUDkYQLQw/iBO9SgYBaNgFIwwAADHr1GIaq1I6AAAAABJRU5ErkJggg==","orcid":"","institution":"Sharda University","correspondingAuthor":true,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Arora","suffix":""},{"id":277443171,"identity":"df0a37d4-f411-4f38-9043-0627856f04b7","order_by":1,"name":"Gouri Sankar Mishra","email":"","orcid":"","institution":"Sharda University","correspondingAuthor":false,"prefix":"","firstName":"Gouri","middleName":"Sankar","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2024-03-07 14:45:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4026454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4026454/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-024-03618-y","type":"published","date":"2025-01-03T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73093244,"identity":"0cf767f3-c2fd-4dda-9f57-fd0bed0762ca","added_by":"auto","created_at":"2025-01-06 16:11:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":745147,"visible":true,"origin":"","legend":"","description":"","filename":"paper1sscopus1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4026454/v1_covered_5c4db892-90b6-46d2-beb4-c326e1aea951.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"NeuroInsight, Brain Tumor Classification, Medical Imaging, Adaptive Deep Learning, Self-Adaptive Framework","lastPublishedDoi":"10.21203/rs.3.rs-4026454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4026454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a robust framework for the classification of brain tumors, beginning with meticulous data curation from 233 patients. 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