An Integrated Deep Convolutional Neural Networks Framework for The Automatic Segmentation and Grading of Glioma Tumors Using Multimodal MRI Scans

An Integrated Deep Convolutional Neural Networks Framework for The Automatic Segmentation and Grading of Glioma Tumors Using Multimodal MRI Scans | 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 An Integrated Deep Convolutional Neural Networks Framework for The Automatic Segmentation and Grading of Glioma Tumors Using Multimodal MRI Scans Otung John Peter Odong, Mohammed Abo-Zahhad, Moataz Abdelwahab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5868181/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Gliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. Timely and precise diagnosis is crucial for effective oncology treatment. Magnetic Resonance Imaging (MRI) facilitates noninvasive assessment of brain lesions. Manual brain tumor evaluation from MRI scans is labor-intensive, relies heavily on clinician experience, and is prone to errors. Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. This study introduces an Integrated Deep Convolutional Neural Network (IDCNN)-based framework for segmenting and grading glioma tumors from multimodal MRI scans. The framework integrates two state-of-the-art CNN architectures. Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading. EfficientNetB2 surpassed other models across all evaluation metrics, achieving 99.19% test accuracy, 99.17% precision, 98.94% sensitivity, 99.57% specificity, and 99.06% F1-score. The optimal EfficientNetB2 model was subsequently utilized to grade tumors identified by the segmentation network. The proposed framework exhibited exceptional performance in both segmentation and grading tasks, attaining dice coefficient scores (DSC) of 86.13, 86.75, and 92.41 in enhancing tumor, tumor core, and whole tumor, respectively, alongside 98.49% classification accuracy for high- and low-grade glioma. Experimental findings validate the superior capabilities of the proposed framework compared to the existing methods. These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making. CAD. MRI. Segmentation. CNN. HGG. LGG. Deep learning. Grading Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 15 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 07 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 28 Mar, 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. 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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-5868181","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439643406,"identity":"b8d3e34f-3cf4-4b71-b9a6-98de71987b4c","order_by":0,"name":"Otung John Peter Odong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2RsWrDMBCGzwiURdTrCYfkFQQGJ3kbl4KndAqYDKkIBDTlEfoQDQHPBoGytJk9JlOWDg7t4KlUjimd7GQsVN8m8X/cfxyAw/F3oSCA5IDNS1wL/yg0rhW8Valj7JK8rvjDdXT8hLvBKFifDxMlJfRWGcL8qVXhio3CPtBw8vy2FVxpBGZShNddqyIMiwIEev9SPGbIVY6A0wg9ZW5RpqeKK4kwfLfKV7fCy0ahtiaxU5hVlouOXZI0AEFDUSQh4l7b5ZLZODZ5q+ITnfFqbgaieDh+YCp9v6c3RbmQrUoNYaJpTuqjXA4Vg+5UvAqa5l75+9k9xeFwOP4V36GrSL7rT/CpAAAAAElFTkSuQmCC","orcid":"","institution":"Egypt-Japan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Otung","middleName":"John Peter","lastName":"Odong","suffix":""},{"id":439643407,"identity":"6b7afceb-4b1e-4641-b1a7-b2472c22428c","order_by":1,"name":"Mohammed Abo-Zahhad","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Abo-Zahhad","suffix":""},{"id":439643408,"identity":"a3a5030f-7741-47af-a1ae-18383dc015a5","order_by":2,"name":"Moataz Abdelwahab","email":"","orcid":"","institution":"Egypt-Japan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Moataz","middleName":"","lastName":"Abdelwahab","suffix":""}],"badges":[],"createdAt":"2025-01-20 18:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5868181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5868181/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80333128,"identity":"93b06a22-131b-4e5d-8a1d-0188f4a43ec2","added_by":"auto","created_at":"2025-04-10 15:50:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":927240,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5868181/v1_covered_87f48129-f2d9-4b60-ad57-81082ede38b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Integrated Deep Convolutional Neural Networks Framework for The Automatic Segmentation and Grading of Glioma Tumors Using Multimodal MRI Scans","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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CAD. MRI. Segmentation. CNN. HGG. LGG. Deep learning. Grading Classification","lastPublishedDoi":"10.21203/rs.3.rs-5868181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5868181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGliomas are the most prevalent and aggressive primary brain tumors characterized by rapid progressions and infiltration. Timely and precise diagnosis is crucial for effective oncology treatment. Magnetic Resonance Imaging (MRI) facilitates noninvasive assessment of brain lesions. Manual brain tumor evaluation from MRI scans is labor-intensive, relies heavily on clinician experience, and is prone to errors. Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. This study introduces an Integrated Deep Convolutional Neural Network (IDCNN)-based framework\u003cstrong\u003e \u003c/strong\u003efor segmenting and grading glioma tumors from multimodal MRI scans. The framework integrates two state-of-the-art CNN architectures. Based on 3D U-Net architecture, the first CNN performs tumor segmentation from multimodal MRI volumes. The segmentation network utilizes the encoder for feature extraction and dimensionality reduction, while the decoder reconstructs the output to the original input size. A thorough performance evaluation of CNN architectures based on pre-trained (ResNet50, EfficientNetB0, DensNet121, EfficientNetB2, and ResNet152V2) was conducted on a three-class dataset of glioma, meningioma, and pituitary tumors to determine the best model for tumor grading.\u003c/p\u003e\n\u003cp\u003eEfficientNetB2 surpassed other models across all evaluation metrics, achieving 99.19% test accuracy, 99.17% precision, 98.94% sensitivity, 99.57% specificity, and 99.06% F1-score. The optimal EfficientNetB2 model was subsequently utilized to grade tumors identified by the segmentation network. The proposed framework exhibited exceptional performance in both segmentation and grading tasks, attaining dice coefficient scores (DSC) of 86.13, 86.75, and 92.41 in enhancing tumor, tumor core, and whole tumor, respectively, alongside 98.49% classification accuracy for high- and low-grade glioma. 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These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making.\u003c/p\u003e","manuscriptTitle":"An Integrated Deep Convolutional Neural Networks Framework for The Automatic Segmentation and Grading of Glioma Tumors Using Multimodal MRI Scans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 15:18:13","doi":"10.21203/rs.3.rs-5868181/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2025-04-15T08:57:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T05:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152553238696346622642298468110399064530","date":"2025-04-12T04:55:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T15:50:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303216232719118901941529150169056446528","date":"2025-04-07T08:23:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-07T06:10:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T08:25:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-03-28T23:26:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffdd319b-a46f-4c72-bf19-0a539b8027b1","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-16T08:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 15:18:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5868181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5868181","identity":"rs-5868181","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}