Secure Medical Image Transmission and Storagein IoT Cloud Using GAN - RBM with Real-TimeAnalysis

Secure Medical Image Transmission and Storagein IoT Cloud Using GAN - RBM with Real-TimeAnalysis | 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 Secure Medical Image Transmission and Storagein IoT Cloud Using GAN - RBM with Real-TimeAnalysis Saranya K, Valarmathi A This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4847590/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 As IoT devices become increasingly integrated into healthcare systems, thesecurity and privacy of medical image data transmitted and stored in cloudenvironments are paramount. This paper introduces a novel approach that harnessesvanilla Generative Adversarial Networks (GANs) and chaotic RestrictedBoltzmann Machines (RBMs) to tackle security challenges in medical imagetransmission and storage. GANs generate privacy-preserving representationsof medical images, while RBMs facilitate secure compression, encryption, andanomaly detection. The suggested GAN-RBM (GAN-RBM) integrates GANsand RBMs into IoT systems to provide the safe and effective transfer of medicalimages over networks that may not be secure. Additionally, it offers strong protectionfor image data saved in the cloud. Therefore, these massive amounts ofmedical data are securely stored, processed, and handled using cloud computingtechnology, which is protected from numerous assaults. Real-time (IntraCranialHaemorrhage -ICH) datasets are collected from hospitals for MRI, and CT imagesare utilized for experimentation. Performance metrics such as encryption/decryptionspeed, compression ratio, and anomaly detection accuracy are evaluated todemonstrate the effectiveness of the proposed approach in safeguarding patient privacy, ensuring data integrity, and detecting unauthorized access or tamperingattempts. Encrypting a 140MB image takes 950 seconds and decrypting 990seconds. For 200 epochs, the proposed model has a compression ratio of 97.34%and GANs-RBM outperformed all other machine-learning models with a 94.17%anomaly detection score. Deep learning Generative Adversarial Networks (GANs) Restricted Boltzmann Machines (RBMs) IntraCranial Haemorrhage 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. 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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-4847590","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338691513,"identity":"4f1fb3a9-343d-465b-8c5f-7b90292a6bd0","order_by":0,"name":"Saranya K","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDADCR7mAwyMDaRpYUsgWQuPAXFa5NvPPvxc2GYnJ9lz5pvEzx02cgzsh49uwKfF4Ey6sfTMtmRjad7ebZK9Z9KMGXjS0m7g1cKQxiDN28acOI+fd5sEb9vhxAYJHjO8WuT7nzH/5m2rB2rheSb5lxgtDDfS2KRBhs/m7YEwCGoxuPGMzZrn3HFjyZ5jxtaybWnGbIT8It+fxnybp6xaTuJM8sObb9ts5PjZDx/D7zAQYGQDUywSIJKNoHIw+AMmmT8Qp3oUjIJRMApGGgAARwZE/5bPcfAAAAAASUVORK5CYII=","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":true,"prefix":"","firstName":"Saranya","middleName":"","lastName":"K","suffix":""},{"id":338691514,"identity":"820f8faf-274a-4899-90c2-6e4567e2df71","order_by":1,"name":"Valarmathi A","email":"","orcid":"","institution":"UCE BIT Campus, Tiruchirappalli","correspondingAuthor":false,"prefix":"","firstName":"Valarmathi","middleName":"","lastName":"A","suffix":""}],"badges":[],"createdAt":"2024-08-02 10:08:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4847590/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4847590/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64002744,"identity":"0f416c71-7c90-4812-99f3-d95acf8b62b0","added_by":"auto","created_at":"2024-09-04 20:30:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2115632,"visible":true,"origin":"","legend":"","description":"","filename":"SecureMedicalimageMachineVisionandApplications.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4847590/v1_covered_432243b1-c9c2-4b08-a2f6-748d3aed113d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Secure Medical Image Transmission and Storagein IoT Cloud Using GAN - RBM with Real-TimeAnalysis","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":"Deep learning, Generative Adversarial Networks (GANs), Restricted Boltzmann Machines (RBMs), IntraCranial Haemorrhage","lastPublishedDoi":"10.21203/rs.3.rs-4847590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4847590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As IoT devices become increasingly integrated into healthcare systems, thesecurity and privacy of medical image data transmitted and stored in cloudenvironments are paramount. This paper introduces a novel approach that harnessesvanilla Generative Adversarial Networks (GANs) and chaotic RestrictedBoltzmann Machines (RBMs) to tackle security challenges in medical imagetransmission and storage. GANs generate privacy-preserving representationsof medical images, while RBMs facilitate secure compression, encryption, andanomaly detection. The suggested GAN-RBM (GAN-RBM) integrates GANsand RBMs into IoT systems to provide the safe and effective transfer of medicalimages over networks that may not be secure. Additionally, it offers strong protectionfor image data saved in the cloud. Therefore, these massive amounts ofmedical data are securely stored, processed, and handled using cloud computingtechnology, which is protected from numerous assaults. Real-time (IntraCranialHaemorrhage -ICH) datasets are collected from hospitals for MRI, and CT imagesare utilized for experimentation. 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