Wavelet-CNN Feature Fusion Architecture for Robust Breast Cancer Classification in Histopathological Imaging

Wavelet-CNN Feature Fusion Architecture for Robust Breast Cancer Classification in Histopathological 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 Wavelet-CNN Feature Fusion Architecture for Robust Breast Cancer Classification in Histopathological Imaging Manvi Bohra, Kamred Udham Singh, Indrajeet Kumar, Mohd Asif Shah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6767943/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Breast cancer remains a leading cause of cancer-related mortality among women, underscoring the critical need for early and accurate diagnostic strategies. In this study, we introduce a multiscale feature fusion framework that integrates the Lifting Wavelet Transform (LWT) with a Multi-Path Convolutional Neural Network (CNN) to enhance the detection of breast cancer in histopathological images. The proposed methodology is evaluated using the BreakHis dataset, which comprises 7,638 histopathology images captured at four magnification levels (40×, 100×, 200×, and 400×), each annotated as either benign or malignant. The processing pipeline begins with image normalization and resizing, followed by the application of a three-level two-dimensional LWT to extract informative low-frequency sub-band components. These wavelet-derived features are subsequently fed into a custom-designed multi-path CNN, where distinct convolutional branches are dedicated to processing features specific to each decomposition level, thereby facilitating more effective lesion classification.Comprehensive experimental analysis demonstrates that the proposed framework achieves superior diagnostic accuracy, outperforming established pre-trained CNN models. Notably, the network attains a testing accuracy of 99.28% when combining images at 40×, 200×, and 400× magnification levels using the Haar wavelet filter. These results substantiate the efficacy of multiscale wavelet-CNN feature fusion for histopathological breast cancer detection, offering a robust approach for early and reliable diagnosis. Breast Cancer Histopathology Image Analysis Deep Learning Convolutional Neural Networks Detection Diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 28 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 21 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 28 May, 2025 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. 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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-6767943","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477925189,"identity":"255d934c-4d68-4c9f-8054-69271babd363","order_by":0,"name":"Manvi Bohra","email":"","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":false,"prefix":"","firstName":"Manvi","middleName":"","lastName":"Bohra","suffix":""},{"id":477925190,"identity":"f3c9ac93-c6c4-4e06-a923-577a56a4095d","order_by":1,"name":"Kamred Udham Singh","email":"","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":false,"prefix":"","firstName":"Kamred","middleName":"Udham","lastName":"Singh","suffix":""},{"id":477925191,"identity":"f33b66f7-979c-4842-b185-7034e09e32dd","order_by":2,"name":"Indrajeet Kumar","email":"","orcid":"","institution":"Birla Global University","correspondingAuthor":false,"prefix":"","firstName":"Indrajeet","middleName":"","lastName":"Kumar","suffix":""},{"id":477925192,"identity":"573b9dff-7022-44d1-b0b2-27d0fce16822","order_by":3,"name":"Mohd Asif Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3OMQrCMBSA4SeFujxwFUrxBEJKIA4GzxIRMiuCOHZzc1YEz9DiBQIZugQ9QBfFC1RcBAeNowgNOjnkhyQk8PEC4PP9a4JzYo/XgkZqt8pNpHwjjZV7jNRfkG4anKujONDWRufXMfA4U8EprSNMhawtRMnaezmNViBppsLEQYDB8FZyMEgiBD3MFPSO9aR5rYTY845Bekd4WNK8OKYgsR9TjBhkdoqyBB0f0zizZEQTE876SEZ0rXFST4rF7nITg2Rrgl2J80G8LBZ5LYHg7UY+Xnw+n8/3S08ubkrTnLI70AAAAABJRU5ErkJggg==","orcid":"","institution":"Kardan University","correspondingAuthor":true,"prefix":"","firstName":"Mohd","middleName":"Asif","lastName":"Shah","suffix":""}],"badges":[],"createdAt":"2025-05-28 12:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6767943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6767943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85641253,"identity":"1415054c-f417-4b58-b0a7-f906002e27f9","added_by":"auto","created_at":"2025-06-30 07:31:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2573481,"visible":true,"origin":"","legend":"","description":"","filename":"LWTspringer3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6767943/v1_covered_43891523-c299-4c63-8b11-e71abe278e79.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wavelet-CNN Feature Fusion Architecture for Robust Breast Cancer Classification in Histopathological Imaging","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":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Histopathology, Image Analysis, Deep Learning, Convolutional Neural Networks, Detection, Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-6767943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6767943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer remains a leading cause of cancer-related mortality among women, underscoring the critical need for early and accurate diagnostic strategies. 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