Bacterial Foraging Optimization Algorithm with Deep Learning Method to EnhanceBreast Cancer Detection using Digital Mammography

Bacterial Foraging Optimization Algorithm with Deep Learning Method to EnhanceBreast Cancer Detection using Digital Mammography | 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 Bacterial Foraging Optimization Algorithm with Deep Learning Method to EnhanceBreast Cancer Detection using Digital Mammography Banumathy D, Karthikeyan D, Mohanraj G, Sarathkumar R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4675148/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 This study focuses on improving the detection of breast cancer at an early stage. The standard approach for diagnosing breast cancer is mammography, but it is pretty tedious as it is subject to subjective analysis. The study will examine how deep learning-based techniques are used in mammography analysis to improve the screening process in order to overcome these obstacles. Various computer vision models, including Visual Geometry Group (VGG) 19, inceptionV3, and custom 20 Convolutional Neural Network (CNN) architecture, are investigated using the Digital Database for Screening Mammography (DDSM) mammogram dataset. The DDSM is widely used for mammographic image analysis in the research community. In the domain of CNNs, the models have demonstrated considerable promise due to their efficacy in various tasks, such as image recog- nition and classification. It is also seen that the CNN model’s performance is enhanced using hyperparameter optimization. However, manually tuning hyper- parameters is laborious and time-consuming. To overcome this challenge, CNN’s automatic hyperparameter optimization uses metaheuristic approaches based on the population. This automation mitigates the time required for finding optimal hyperparameters and boosts the CNN model’s efficacy. The proposed approach uses the Bacterial Foraging Optimization (BFO) algorithm to optimize CNN to enhance breast cancer detection. BFO is utilized to optimize various hyperparam- eters, such as filter size, number of filters, and hidden layers in the CNN model. It is demonstrated through experiments that the proposed BFO-CNN method achieves better performance than other state-of-the-art methods by 7.62% for the VGG 19, by 9.16% for the inceptionV3, and by 1.78% for the custom CNN- 20 layers. In conclusion, this work aims to leverage deep learning techniques and automatic hyperparameter optimization to enhance breast cancer detec- tion through mammogram analysis. The BFO-CNN model has much potential to improve breast cancer diagnosis accuracy compared to conventional CNN architecture. Breast Cancer Mammography Visual Geometry Group 19 InceptionV3 Convolutional Neural Network and Bacterial Foraging Optimization 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4675148","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328855836,"identity":"5af0426d-a546-49e5-9f64-e2a67da69e20","order_by":0,"name":"Banumathy D","email":"data:image/png;base64,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","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":true,"prefix":"","firstName":"Banumathy","middleName":"","lastName":"D","suffix":""},{"id":328855837,"identity":"5fd9ad71-d154-4bd3-bde9-bc91bc9fde44","order_by":1,"name":"Karthikeyan D","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Karthikeyan","middleName":"","lastName":"D","suffix":""},{"id":328855838,"identity":"b90efed7-8014-42ce-b46e-0d71c293383f","order_by":2,"name":"Mohanraj G","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Mohanraj","middleName":"","lastName":"G","suffix":""},{"id":328855839,"identity":"08213e45-4a17-4de9-b6e6-e7434d2e3e19","order_by":3,"name":"Sarathkumar R","email":"","orcid":"","institution":"victorian institute of te","correspondingAuthor":false,"prefix":"","firstName":"Sarathkumar","middleName":"","lastName":"R","suffix":""}],"badges":[],"createdAt":"2024-07-02 14:59:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4675148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4675148/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61495639,"identity":"df8a60bf-0ac7-483d-ab32-840df1c1e4b3","added_by":"auto","created_at":"2024-07-31 11:44:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":559067,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate41.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4675148/v1_covered_839af30f-73b4-4024-ab2b-2b29f54d2f1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bacterial Foraging Optimization Algorithm with Deep Learning Method to EnhanceBreast Cancer Detection using Digital Mammography","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Breast Cancer, Mammography, Visual Geometry Group 19, InceptionV3, Convolutional Neural Network and Bacterial Foraging Optimization","lastPublishedDoi":"10.21203/rs.3.rs-4675148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4675148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on improving the detection of breast cancer at an early stage. 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To overcome this challenge, CNN’s automatic hyperparameter optimization uses metaheuristic approaches based on the population. This automation mitigates the time required for finding optimal hyperparameters and boosts the CNN model’s efficacy. The proposed approach uses the Bacterial Foraging Optimization (BFO) algorithm to optimize CNN to enhance breast cancer detection. BFO is utilized to optimize various hyperparam- eters, such as filter size, number of filters, and hidden layers in the CNN model. It is demonstrated through experiments that the proposed BFO-CNN method achieves better performance than other state-of-the-art methods by 7.62% for the VGG 19, by 9.16% for the inceptionV3, and by 1.78% for the custom CNN- 20 layers. In conclusion, this work aims to leverage deep learning techniques and automatic hyperparameter optimization to enhance breast cancer detec- tion through mammogram analysis. 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