Optimize Multiscale Feature Hybrid-Net Deep Learning Approach Used for Automatic Pancreas Image Segmentation

Optimize Multiscale Feature Hybrid-Net Deep Learning Approach Used for Automatic Pancreas Image 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 Research Article Optimize Multiscale Feature Hybrid-Net Deep Learning Approach Used for Automatic Pancreas Image Segmentation Dr. Pradip Paithane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3945678/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 The accurate and clean pancreatic segmentation problem is a significant and difficult challenge in the research ofmedical imaging. The proposed approach is helpful in Bottom-Up techniques for quickly and accurately identifyingpancreatic cancer-affected areas in the kidney and pancreas. The proposed approach is employed for multiple organdetection with fine outliers from Computed Tomography scan images with sharp precision. In the proposed approach,19 layers are used with 4 convolution layers. The optimized multiscale feature hybrid block is utilized. The convolutionlayer incorporates the small feature block with a similar or different size of kernel block. The dense convolution isdivided into sub-convolution layers with their own ”Batch normalization” layer. The convolution operation is performedon sub-convolution layers with the ”Swish activation” function. The aggregates function only uses features that arenecessary, causing unused data to be dropped at the convolution layer. The Validation Accuracy(98.82% ), precisionvalue (84.14±19.03) Training global accuracy (95.486%) values are improvised as compared to State-of-Art. Theproposed approach achieved a dice similarity index score upto 90.29±84.34%. During testing, the segmentation ofmedical images by the proposed approach, takes 1 to 2 seconds. In deep learning approaches, many convolution layersare added to the model to achieve high accuracy but training as well as unnecessary computational time also increases.The proposed approach overcomes this drawback with an optimized convolution layer number. The proposed approachis considering the only hybrid sub-convolution layer with required multiscale feature blocks only Pancreatic Cancer Deep Learning Convolution neural network Image Segmentation Multiscale Hybrid Swish Activation 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-3945678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272850338,"identity":"c81c6b70-fd6d-4735-abed-8e4a730f6b32","order_by":0,"name":"Dr. Pradip Paithane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFACxgZmGFMigcEGJNJ4gICWxmYkLWlgQwhoYWBEaGFgOAxm4NVizn64/XFBzR05c+nDD2883HHebm37YaAtNTbRuLRY9iQ2Ns849szYsi/N2CLxzO3kbWcSgVqOpeU24NBicACohYftcOKGMwxmEoltt5PNDgC1MDYcxq3l/EOgln+H6zecYf8G1HIu2ez8QwJabgBt4W07nGBwhgdkywE7sxsEbLGc8bBxNm/fYcOdPTzFQL8kJ5jdANqSgMcv5vzpDz7zfDssb87DvvHmzx129mbn0x8++FBjg9thKAzGBoZEsMoEHMqxarHHo3gUjIJRMApGKAAAhIZquO/hvQAAAAAASUVORK5CYII=","orcid":"","institution":"VPKBIET","correspondingAuthor":true,"prefix":"Dr.","firstName":"Pradip","middleName":"","lastName":"Paithane","suffix":""}],"badges":[],"createdAt":"2024-02-10 10:59:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3945678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3945678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51943851,"identity":"c9094326-e5d9-4a83-8ab4-fc8bf0f91f5d","added_by":"auto","created_at":"2024-03-04 10:01:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":953269,"visible":true,"origin":"","legend":"","description":"","filename":"MultimediaToolOPMFLayerSCI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3945678/v1_covered_b6ebc851-04f9-477e-9b8e-d86a8ec669f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimize Multiscale Feature Hybrid-Net Deep Learning Approach Used for Automatic Pancreas Image Segmentation","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":"Pancreatic Cancer, Deep Learning, Convolution neural network, Image Segmentation, Multiscale Hybrid, Swish Activation","lastPublishedDoi":"10.21203/rs.3.rs-3945678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3945678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The accurate and clean pancreatic segmentation problem is a significant and difficult challenge in the research ofmedical imaging. 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