Energy-preserving Quantum-inspired Wavelet Features for Mri Abnormality Detection

Energy-preserving Quantum-inspired Wavelet Features for Mri Abnormality Detection | 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 Energy-preserving Quantum-inspired Wavelet Features for Mri Abnormality Detection Jyothula Sunil Kumar, Sowdamini Nekkalapudi Durga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8984635/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 Identification of abnormalities correctly in magnetic resonance imaging (MRI) is a challenging problem, especially in clinical situations with limited annotated data. Here, a quantum-inspired multi-scale wavelet energy framework is proposed for automated MRI abnormality detection and localization. Framework combines principles from quantum mechanics, with classical signal processing, to achieve stable, energy-preserving and interpretable representations of features. Firstly, MRI images are pre-processed and decomposed in overlapping patches using a sliding-window strategy. Each patch is projected onto a state representation in a quantum-inspired way, by constraining the energy to 1, which allows for the analysis in a Hilbert space. Orthonormal wavelet transforms (Haar and Daubechies-2 (db2)) are used as energy preserving unitary transformation to project the normalized patches to frequency subspaces. High frequency wavelet sub-band energies are computed for characterization of structural irregularities linked to pathological tissue. To overcome the problem of the size variability of the damaged tissue, a multi-scale analysis is conducted based on 8X8, 16X16 and 32X32 pixel patch sizes. Spatial energy maps are created and fused for use in localization of damaged tissue. A relatively small and understandable feature vector is created through the fusion of patch-level statistics, wavelet energy distributions, high-frequency to low-frequency energy ratios, and damaged tissue concentration measures. Classification is done with support vector machine (SVM) and radial basis function kernel. Experimental results show a definite scale-wavelet dependency. Extremely small patches (p = 8) result in high sensitivity, but less specificity from noise amplification. The db2 wavelet gives the best performance at p = 16, where it achieves an accuracy of 72.60%, a sensitivity of 89.13%, and an F1-score of 0.8039, which shows that the capability of detecting the abnormality is very good. In contrast, the Haar wavelet shows more stable behaviour at larger patch sizes (p = 32) which offers a balanced sensitivity/specificity trade-off. The proposed framework results highlights the importance of adaptive scale and wavelet selection in energy-based MRI abnormality detection. Quantum-inspired normalization Wavelet sub-band energy Multi-scale signal analysis Patch-based MRI analysis Haar and db2 wavelets Pattern classification 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. 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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-8984635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600321244,"identity":"720f0523-783b-4cfb-a845-76eb23441799","order_by":0,"name":"Jyothula Sunil Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACxgY2MM1sP//xASAtIUO0FnYDhrQEkBYeIuyBaOE3YMgxADEIa2HuP5YmdaPmnrQ5w5nPr27UWPAwsB8+ugGvw2akHZPOOVZsbNnYu8065xjQYTxpaTfwa2Fvk85hS0hmOMy7zTiHDahFgscMv5b+40At/xLqG47xPDPO+UeMlgagw3LbEpgNzvAwP85tI0bLjLRk69y+BGbJGWxmzLl9EjxshPxi2H/M8HbOtwRmfgnmx59zvtXJ8bMfPoZfSwOCzSYBJvEpBwF5JDbzB0KqR8EoGAWjYGQCAM2jQ9K1PrzkAAAAAElFTkSuQmCC","orcid":"","institution":"Malla Reddy (MR) Deemed to be University","correspondingAuthor":true,"prefix":"","firstName":"Jyothula","middleName":"Sunil","lastName":"Kumar","suffix":""},{"id":600321245,"identity":"e48f67b0-3067-45d2-a6cf-36cb6cb0986f","order_by":1,"name":"Sowdamini Nekkalapudi Durga","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sowdamini","middleName":"Nekkalapudi","lastName":"Durga","suffix":""}],"badges":[],"createdAt":"2026-02-27 07:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8984635/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8984635/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707641,"identity":"464d5921-a4cb-442b-b0d1-e239a85d9db4","added_by":"auto","created_at":"2026-04-24 09:20:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":747754,"visible":true,"origin":"","legend":"","description":"","filename":"ENERGYPRESERVINGQUANTUMINSPIREDWAVELETFEATURESFORMRIABNORMALITYDETECTION.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8984635/v1_covered_0fd16634-807d-4602-8c1f-9b45e5db84dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnergy-preserving Quantum-inspired Wavelet Features for Mri Abnormality Detection\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":"Quantum-inspired normalization, Wavelet sub-band energy, Multi-scale signal analysis, Patch-based MRI analysis, Haar and db2 wavelets, Pattern classification","lastPublishedDoi":"10.21203/rs.3.rs-8984635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8984635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdentification of abnormalities correctly in magnetic resonance imaging (MRI) is a challenging problem, especially in clinical situations with limited annotated data. Here, a quantum-inspired multi-scale wavelet energy framework is proposed for automated MRI abnormality detection and localization. Framework combines principles from quantum mechanics, with classical signal processing, to achieve stable, energy-preserving and interpretable representations of features.\u003c/p\u003e \u003cp\u003eFirstly, MRI images are pre-processed and decomposed in overlapping patches using a sliding-window strategy. Each patch is projected onto a state representation in a quantum-inspired way, by constraining the energy to 1, which allows for the analysis in a Hilbert space. Orthonormal wavelet transforms (Haar and Daubechies-2 (db2)) are used as energy preserving unitary transformation to project the normalized patches to frequency subspaces. High frequency wavelet sub-band energies are computed for characterization of structural irregularities linked to pathological tissue. To overcome the problem of the size variability of the damaged tissue, a multi-scale analysis is conducted based on 8X8, 16X16 and 32X32 pixel patch sizes. Spatial energy maps are created and fused for use in localization of damaged tissue.\u003c/p\u003e \u003cp\u003eA relatively small and understandable feature vector is created through the fusion of patch-level statistics, wavelet energy distributions, high-frequency to low-frequency energy ratios, and damaged tissue concentration measures. Classification is done with support vector machine (SVM) and radial basis function kernel. Experimental results show a definite scale-wavelet dependency. Extremely small patches (p\u0026thinsp;=\u0026thinsp;8) result in high sensitivity, but less specificity from noise amplification. The db2 wavelet gives the best performance at p\u0026thinsp;=\u0026thinsp;16, where it achieves an accuracy of 72.60%, a sensitivity of 89.13%, and an F1-score of 0.8039, which shows that the capability of detecting the abnormality is very good. In contrast, the Haar wavelet shows more stable behaviour at larger patch sizes (p\u0026thinsp;=\u0026thinsp;32) which offers a balanced sensitivity/specificity trade-off. 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