Research on Liver Cancer Pathology Image Recognition Based on Deep Learning Image Processing

Research on Liver Cancer Pathology Image Recognition Based on Deep Learning Image Processing | 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 Research on Liver Cancer Pathology Image Recognition Based on Deep Learning Image Processing Mingyuan Yang, Huanzhang Niu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6455027/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract The accurate diagnosis of lung cancer through pathological image analysis remains challenging due to the complexity and heterogeneity of histopathological features. Current deep learning approaches often fail to comprehensively capture the diverse visual characteristics present in tissue samples, limiting their diagnostic accuracy. This study proposes MSAF-Net, a novel multi-space attention fusion network that systematically integrates five complementary feature spaces (R, B, Y, entropy, and LBP) with an SE-block enhanced fusion mechanism and EfficientNet-Lite based feature extraction. Experimental results demonstrate superior performance with 94.7% accuracy, 93.2% sensitivity, and 95.8% specificity, representing significant improvements of 6.3%, 7.1%, and 5.6% respectively over conventional single-space methods. The proposed framework establishes a new state-of-the-art in pathological image analysis by effectively combining engineered feature spaces with deep learning, offering both high diagnostic reliability and computational efficiency for clinical applications. Biological sciences/Cancer/Cancer imaging Physical sciences/Mathematics and computing lung cancer diagnosis pathological image analysis MSAF-Net multi-space feature fusion deep learning attention mechanisms clinical pathology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviews received at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviews received at journal 27 Jun, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 18 Jun, 2025 Submission checks completed at journal 09 May, 2025 First submitted to journal 09 May, 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. 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-6455027","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477589490,"identity":"242fcd09-b563-40e2-8499-c5276040bdc5","order_by":0,"name":"Mingyuan Yang","email":"data:image/png;base64,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","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Mingyuan","middleName":"","lastName":"Yang","suffix":""},{"id":477589491,"identity":"9bd8321d-7390-45bf-adf5-c6ffc639a04b","order_by":1,"name":"Huanzhang Niu","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Huanzhang","middleName":"","lastName":"Niu","suffix":""}],"badges":[],"createdAt":"2025-04-15 12:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6455027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6455027/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24834-7","type":"published","date":"2026-01-02T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99545265,"identity":"fd1bacfb-357f-43f3-8209-02a08f74a556","added_by":"auto","created_at":"2026-01-05 16:04:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":916559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript20250423.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6455027/v1_covered_af730efa-69ba-4e83-a45e-7f4f1bb99a40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Liver Cancer Pathology Image Recognition Based on Deep Learning Image Processing","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":"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":"lung cancer diagnosis, pathological image analysis, MSAF-Net, multi-space feature fusion, deep learning, attention mechanisms, clinical pathology","lastPublishedDoi":"10.21203/rs.3.rs-6455027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6455027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accurate diagnosis of lung cancer through pathological image analysis remains challenging due to the complexity and heterogeneity of histopathological features. Current deep learning approaches often fail to comprehensively capture the diverse visual characteristics present in tissue samples, limiting their diagnostic accuracy. This study proposes MSAF-Net, a novel multi-space attention fusion network that systematically integrates five complementary feature spaces (R, B, Y, entropy, and LBP) with an SE-block enhanced fusion mechanism and EfficientNet-Lite based feature extraction. Experimental results demonstrate superior performance with 94.7% accuracy, 93.2% sensitivity, and 95.8% specificity, representing significant improvements of 6.3%, 7.1%, and 5.6% respectively over conventional single-space methods. The proposed framework establishes a new state-of-the-art in pathological image analysis by effectively combining engineered feature spaces with deep learning, offering both high diagnostic reliability and computational efficiency for clinical applications.\u003c/p\u003e","manuscriptTitle":"Research on Liver Cancer Pathology Image Recognition Based on Deep Learning Image Processing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 05:50:57","doi":"10.21203/rs.3.rs-6455027/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T19:56:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T10:38:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T06:17:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T02:18:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129268676394126328840031533875884843913","date":"2025-06-30T01:37:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-28T01:29:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T19:21:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221997126186218979737914044880768353430","date":"2025-06-26T21:01:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160094064267869742159950037459565469284","date":"2025-06-26T09:49:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326844307314073799797643942917779096895","date":"2025-06-26T08:42:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240558031293958483762080484730562125724","date":"2025-06-26T08:08:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324282953271666330320478638633033408390","date":"2025-06-26T07:34:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-26T07:25:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T05:44:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-09T05:17:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-09T05:16:34+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":"09f171c2-14f1-4c36-a1bb-3801278af004","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50717731,"name":"Biological sciences/Cancer/Cancer imaging"},{"id":50717732,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-05T15:59:56+00:00","versionOfRecord":{"articleIdentity":"rs-6455027","link":"https://doi.org/10.1038/s41598-025-24834-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-02 15:57:37","publishedOnDateReadable":"January 2nd, 2026"},"versionCreatedAt":"2025-06-30 05:50:57","video":"","vorDoi":"10.1038/s41598-025-24834-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24834-7","workflowStages":[]},"version":"v1","identity":"rs-6455027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6455027","identity":"rs-6455027","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}