Screening Glioma and Glioblastoma Brain Tumors using Dual Deep Learning Algorithm incorporated Correlative GAN and BrainNet through the Probability Segmentation

Screening Glioma and Glioblastoma Brain Tumors using Dual Deep Learning Algorithm incorporated Correlative GAN and BrainNet through the Probability Segmentation | 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 Screening Glioma and Glioblastoma Brain Tumors using Dual Deep Learning Algorithm incorporated Correlative GAN and BrainNet through the Probability Segmentation Mohana Sundari L, Senthil Kumar T, Rajkumar M, Karthikeyan D This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7251339/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The earlier identification of the tumors in human brain can improve the life time of the affected patients. Mainly, Glioma and Glioblastoma are the primary type of brain tumors where the survival rate of the patient is low and hence it’s earlier screening is important. This research work proposes Dual Deep Learning (DDL) based Glioma and Glioblastoma brain tumor detection methodology. The main objective of this research work is for performing multi class brain image classification process. The proposed tumor detection system contains preprocessing, data augmentation and the proposed DDL algorithm module in training of the system for generating the training values. The testing system of the proposed work contains preprocessing, the proposed DDL algorithm module along with the probability segmentation algorithm to perform both classification and segmentation process. The preprocessing is used here to enhance the brain imaging quality to improve the tumor detection performance and the data augmentation increases the brain images count for neglecting the issues of the overfitting during the training stage of the classifier only. The proposed DDL algorithm module is designed with Correlative Generative Adversarial Networks (CGAN) and BrainNet classification algorithms, where as CGAN is proposed for computing the discriminative features which are mainly used for differentiating the Glioma and Glioblastoma. The computed discriminative features are classified by the proposed BrainNet classification algorithm which produces the classification results. The Empirical-Axiomatic Probability Segmentation Algorithm (EAPSA) have been constructed for segmenting the region of tumor pixels in both Glioma and Glioblastoma images. The ablation parameter study of the proposed DDL classification algorithm is performed and its experimental results are achieved by testing the different brain MRI images which are available on standard benchmarked brain MRI imaging datasets. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Health sciences/Oncology Brain Glioma Glioblastoma classifier segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 Apr, 2026 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 08 Sep, 2025 Editor invited by journal 08 Aug, 2025 Submission checks completed at journal 06 Aug, 2025 First submitted to journal 06 Aug, 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. We do this by developing innovative software and high quality services for the global research community. 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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-7251339","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":556781429,"identity":"9f2c9a38-5e84-4144-af79-7600a3c9c64a","order_by":0,"name":"Mohana Sundari L","email":"","orcid":"","institution":"VIT University","correspondingAuthor":false,"prefix":"","firstName":"Mohana","middleName":"Sundari","lastName":"L","suffix":""},{"id":556781430,"identity":"84f21133-8d1c-438f-b136-642c138572f9","order_by":1,"name":"Senthil Kumar 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