Multi-path Convolutional Neural Network to identify Tumorous Sub-classes for Breast Tissue from Histopathological Images

Multi-path Convolutional Neural Network to identify Tumorous Sub-classes for Breast Tissue from Histopathological Images | 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 Multi-path Convolutional Neural Network to identify Tumorous Sub-classes for Breast Tissue from Histopathological Images Rangan Das, Utsav Bandyopadhyay Maulik, Bikram Boote, Sagnik Sen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1080617/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 Malignancy is one of the leading causes of death globally. It is on the rise in the developed and low-income countries with survival rates of less than 40%. However, early diagnosis may increase survival chances. Histopathology images acquired from the biopsy are a popular method for cancer diagnosis. In this article, we propose a deep convolutional neural network-based method that helps classify breast cancer tumor subtypes from histopathology images. The model is trained on the BreakHis dataset but is also tested on images from other datasets. The model is trained to recognized eight different tumor subtypes, and also to perform binary classification (malignant / non-malignant). The CNN model uses an encoder-decoder architecture as well as a parallel feed-forward network. The proposed model provides higher cumulative training accuracy and statistical scoring after five-fold cross-validation. Comparing with the other models, the accuracy of the proposed model is higher at different magnification and patient levels. Pathology Nuclear Medicine & Medical Imaging Breast Histopathology Deep Learning Convolutional Neural Networks Image Classification Full Text 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-1080617","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":66564557,"identity":"57887032-f276-4a2f-96db-650087417b88","order_by":0,"name":"Rangan Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYPACGxk2EJVAgpY0HpK1HOYhXq15+9nDL37uOM/DJ5F8gOHhDiK0yJzJS7PsPXObh00iLYEh8QwRWiQYcswMeNuAWnjOGDAkthGjhf+NmeHftnNALec/EKlFIsf4MW/bAR429h4GYrW8MWOWbUsGamkzOECkw3KMP75ts5OTb2Z++PAnMVqAgE0CxjpAnAYGBuYPxKocBaNgFIyCEQoAipQu1/jnaQQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6867-6136","institution":"Jadavpur University Department of Computer Science and Engineering","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rangan","middleName":"","lastName":"Das","suffix":""},{"id":66564558,"identity":"a34bfb18-2155-40e7-a8a5-1d7b66804465","order_by":1,"name":"Utsav Bandyopadhyay Maulik","email":"","orcid":"","institution":"Techno India Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Utsav","middleName":"Bandyopadhyay","lastName":"Maulik","suffix":""},{"id":66564559,"identity":"dea062b8-fe50-49ae-b175-31cec9d32984","order_by":2,"name":"Bikram Boote","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bikram","middleName":"","lastName":"Boote","suffix":""},{"id":66564560,"identity":"a07e29ab-c624-480b-9fc0-1bff99ea5696","order_by":3,"name":"Sagnik Sen","email":"","orcid":"","institution":"Jadavpur University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sagnik","middleName":"","lastName":"Sen","suffix":""},{"id":66564561,"identity":"f699aecb-0382-4821-8812-8dc2d1e55c1a","order_by":4,"name":"Saumik Bhattacharya","email":"","orcid":"","institution":"IIT Kharagpur: Indian Institute of Technology Kharagpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saumik","middleName":"","lastName":"Bhattacharya","suffix":""}],"badges":[],"createdAt":"2021-11-15 06:07:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1080617/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1080617/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16079601,"identity":"b6b9174d-8c63-4868-9df7-0a10a0c3669b","added_by":"auto","created_at":"2021-12-01 17:40:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":782061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1080617/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Multi-path Convolutional Neural Network to identify Tumorous Sub-classes for Breast Tissue from Histopathological Images","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1080617/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"Breast Histopathology, Deep Learning, Convolutional Neural Networks, Image Classification","lastPublishedDoi":"10.21203/rs.3.rs-1080617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1080617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Malignancy is one of the leading causes of death globally. It is on the rise in the developed and low-income countries with survival rates of less than 40%. However, early diagnosis may increase survival chances. Histopathology images acquired from the biopsy are a popular method for cancer diagnosis. In this article, we propose a deep convolutional neural network-based method that helps classify breast cancer tumor subtypes from histopathology images. The model is trained on the BreakHis dataset but is also tested on images from other datasets. The model is trained to recognized eight different tumor subtypes, and also to perform binary classification (malignant / non-malignant). The CNN model uses an encoder-decoder architecture as well as a parallel feed-forward network. The proposed model provides higher cumulative training accuracy and statistical scoring after five-fold cross-validation. Comparing with the other models, the accuracy of the proposed model is higher at different magnification and patient levels.","manuscriptTitle":"Multi-path Convolutional Neural Network to identify Tumorous Sub-classes for Breast Tissue from Histopathological Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-01 17:39:51","doi":"10.21203/rs.3.rs-1080617/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9058473b-9bc8-47f9-ab55-0f2cfa7a07d6","owner":[],"postedDate":"December 1st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8877781,"name":"Pathology"},{"id":8877782,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2021-12-01T17:39:52+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-01 17:39:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1080617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1080617","identity":"rs-1080617","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}