Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram Classification

Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram 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 Research Article Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram Classification Leena Prema Kumari, Perumal Karupaswamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4458881/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2024 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 8 You are reading this latest preprint version Abstract Breast cancer (BC) is proliferating to a greater extent day to day. Early discovery can help the patient in saving life. Mammography is commonly utilized in diagnosing BC. Mammography classification is the most critical stage as it assists medical professionals in identifying BC. In this paper, a hybrid method accomplishes accurate and precise classification in a timely manner is proposed and tested. In this work a method called, Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized (LMHG-TSO) VGG16-based mammogram classification is proposed. The LMHG-TSO VGG16-based mammogram classification method is split into four sections, namely, preprocessing, segmentation, feature selection and finally classification. First, with the raw mammogram images obtained as input is subjected to Laplacian Neighborhood Gabor Filter-based preprocessing for acquiring computationally efficient preprocessed mammogram images. Second watershed segmentation model called Metropolis Hastings Gradient Region is applied to the preprocessed images to obtain precise segmentation results. Next, to minimize dimensionality and reduce loss Torgerson Scaling Sequential Feature Selection model is applied to the segmented preprocessed mammogram images. Finally, Affine Linear Optimized VGG16-based Mammogram Classification model is applied to make distinct classification between three types (i.e., normal, benign and malignant) in an accurate manner. Experimental evaluation is carried out on factors such as precision, recall, training time and classification accuracy for different mammogram images. The reported results prove the efficiency of the suggested method against prevailing stateof-the-art methods. Laplacian Sliding Neighborhood Gabor Filter Metropolis Hastings Gradient Watershed Segmentation Torgerson Scaling Sequential Feature Selection Affine Linear Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2024 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Revision requested 03 Jun, 2024 Reviews received at journal 03 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers invited by journal 24 May, 2024 Editor assigned by journal 23 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 22 May, 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. 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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-4458881","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310130024,"identity":"3bfa4b98-a3a5-41c8-b5a9-8f484726ac1b","order_by":0,"name":"Leena Prema Kumari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCeYGECXHxsx88AGQwcNHWAsjWIsxP3tbsgFICxuxWhJn9pwxkwCxCGrhn93Y+ODjDjvGDTfS0iq/5tjJsDEwP3x0A58ldw42G848k8xscCP52G3ZbclAh7EZG+fgs+ZGYps0bxszmwHQltuS25iBWnjYpPFpkb+R2P77b1s9j8GNHLNiyW31hLUYAG1hZmw7LCEJ9D7jx22HCWsxBPpFsrftuAEokKUZtx3nYWMm4Be5280HP/xsq65vA0blx5/bqu352ZsfPsbrfWTAzAMmiVUOAow/SFE9CkbBKBgFIwYAAErTSpjlt/Q8AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Leena","middleName":"Prema","lastName":"Kumari","suffix":""},{"id":310130025,"identity":"e83256a4-bfd2-41bf-a214-57b76590c8b3","order_by":1,"name":"Perumal Karupaswamy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Perumal","middleName":"","lastName":"Karupaswamy","suffix":""}],"badges":[],"createdAt":"2024-05-22 07:16:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4458881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4458881/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41060-024-00662-2","type":"published","date":"2024-10-31T16:13:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68206929,"identity":"0a5d391c-83f2-44a1-bcc9-59efe50f8fa1","added_by":"auto","created_at":"2024-11-04 16:33:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":413540,"visible":true,"origin":"","legend":"","description":"","filename":"LMHGTSOVGG16BASEDMAMMOGRAMCLASSIFICATION1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4458881/v1_covered_dcf99d0e-119a-4dbb-92d3-debdf0938a44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLaplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram Classification\u003c/p\u003e","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-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Laplacian, Sliding Neighborhood, Gabor Filter, Metropolis Hastings, Gradient Watershed Segmentation, Torgerson Scaling, Sequential Feature Selection, Affine Linear","lastPublishedDoi":"10.21203/rs.3.rs-4458881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4458881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer (BC) is proliferating to a greater extent day to day. 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Second watershed segmentation model called Metropolis Hastings Gradient Region is applied to the preprocessed images to obtain precise segmentation results. Next, to minimize dimensionality and reduce loss Torgerson Scaling Sequential Feature Selection model is applied to the segmented preprocessed mammogram images. Finally, Affine Linear Optimized VGG16-based Mammogram Classification model is applied to make distinct classification between three types (i.e., normal, benign and malignant) in an accurate manner. Experimental evaluation is carried out on factors such as precision, recall, training time and classification accuracy for different mammogram images. 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