SPOHE-VGGNet: Segmentation using Particle Swarm Optimized Histogram Equalization and a Modified U-Net with VGG-19 for Brain Tumor Detection

SPOHE-VGGNet: Segmentation using Particle Swarm Optimized Histogram Equalization and a Modified U-Net with VGG-19 for Brain Tumor 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 Article SPOHE-VGGNet: Segmentation using Particle Swarm Optimized Histogram Equalization and a Modified U-Net with VGG-19 for Brain Tumor Detection Shoffan Saifullah, Rafał Dreżewski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6870596/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Accurate brain tumor segmentation in MRI scans is critical for diagnosis and treatment planning. Conventional methods are often time-consuming, error-prone, and subject to operator bias. Although recent deep-learning approaches show promise for automating medical image segmentation, challenges such as interpretability hinder their clinical adoption. To address these issues, we propose SPOHE-VGGNet, a novel segmentation model that integrates a modified U-Net architecture with VGG-19 and Particle Swarm Optimized Histogram Equalization (PSO-HE). This approach enhances MRI contrast and achieves state-of-the-art segmentation accuracy. PSO-HE outperforms traditional and PSO-based image enhancement techniques, delivering superior image quality and facilitating precise feature extraction by VGG-19. We evaluated SPOHE-VGGNet on the Figshare T1-CE MRI dataset (3,064 images encompassing meningioma, glioma, and pituitary tumor classes) and the multi-modal BraTS 2021 dataset (1,251 images across four MRI modalities focusing on whole tumor masks). On the Figshare dataset, the model achieved Dice Similarity Coefficients (DSC) of 0.9522 for meningioma, 0.9428 for glioma, and 0.9518 for pituitary tumors. For whole tumor segmentation on the BraTS dataset, it attained a DSC of 0.9463. Performance metrics for Figshare included accuracy, precision, recall, and F1-score of 0.9989, 0.9680, 0.9647, and 0.9662, respectively. On the BraTS dataset, these metrics were 0.9977, 0.9544, 0.9738, and 0.9640, underscoring the model’s robustness across different imaging modalities. By combining high performance with interpretability, SPOHE-VGGNet provides a reliable and accurate tool for brain tumor segmentation, advancing clinical decision-making. Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Computational biology and bioinformatics/Machine learning Brain Tumor Segmentation Histogram Equalization Particle Swarm Optimization Deep Learning Medical Imaging Bioinformatics Image Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 04 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 21 Jul, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 11 Jun, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6870596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531874482,"identity":"51ced482-33f1-4247-8457-5c1979081884","order_by":0,"name":"Shoffan 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Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Brain Tumor Segmentation, Histogram Equalization, Particle Swarm Optimization, Deep Learning, Medical Imaging, Bioinformatics, Image Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6870596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6870596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate brain tumor segmentation in MRI scans is critical for diagnosis and treatment planning. Conventional methods are often time-consuming, error-prone, and subject to operator bias. Although recent deep-learning approaches show promise for automating medical image segmentation, challenges such as interpretability hinder their clinical adoption. To address these issues, we propose SPOHE-VGGNet, a novel segmentation model that integrates a modified U-Net architecture with VGG-19 and Particle Swarm Optimized Histogram Equalization (PSO-HE). This approach enhances MRI contrast and achieves state-of-the-art segmentation accuracy. PSO-HE outperforms traditional and PSO-based image enhancement techniques, delivering superior image quality and facilitating precise feature extraction by VGG-19. We evaluated SPOHE-VGGNet on the Figshare T1-CE MRI dataset (3,064 images encompassing meningioma, glioma, and pituitary tumor classes) and the multi-modal BraTS 2021 dataset (1,251 images across four MRI modalities focusing on whole tumor masks). On the Figshare dataset, the model achieved Dice Similarity Coefficients (DSC) of 0.9522 for meningioma, 0.9428 for glioma, and 0.9518 for pituitary tumors. For whole tumor segmentation on the BraTS dataset, it attained a DSC of 0.9463. Performance metrics for Figshare included accuracy, precision, recall, and F1-score of 0.9989, 0.9680, 0.9647, and 0.9662, respectively. On the BraTS dataset, these metrics were 0.9977, 0.9544, 0.9738, and 0.9640, underscoring the model\u0026rsquo;s robustness across different imaging modalities. By combining high performance with interpretability, SPOHE-VGGNet provides a reliable and accurate tool for brain tumor segmentation, advancing clinical decision-making.\u003c/p\u003e","manuscriptTitle":"SPOHE-VGGNet: Segmentation using Particle Swarm Optimized Histogram Equalization and a Modified U-Net with VGG-19 for Brain Tumor Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 06:45:17","doi":"10.21203/rs.3.rs-6870596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T04:52:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T01:19:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58648271759890386511018047339355430343","date":"2026-04-22T08:49:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105835080742489104993606934492190283309","date":"2026-03-25T15:41:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T06:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18596896521997725411139394964859409100","date":"2026-01-05T03:14:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146799412909647911860695728120510078070","date":"2026-01-02T14:02:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133451582819049283800653277209003666520","date":"2025-10-16T16:58:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T13:25:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-21T18:38:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T07:48:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T05:22:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-11T10:03:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21e7a9fd-e5d7-4297-b531-f6641355ec95","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56542984,"name":"Biological sciences/Computational biology and bioinformatics/Image processing"},{"id":56542985,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2026-05-11T09:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 06:45:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6870596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6870596","identity":"rs-6870596","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}