Gallbladder Cancer Detection via Ultrasound Image Analysis: An End-to-End Hierarchical Feature-Fused Model

Gallbladder Cancer Detection via Ultrasound Image Analysis: An End-to-End Hierarchical Feature-Fused Model | 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 Gallbladder Cancer Detection via Ultrasound Image Analysis: An End-to-End Hierarchical Feature-Fused Model Sara Dadjouy, Hedieh Sajedi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5014269/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 Gallbladder cancer is a fatal disease, and its early diagnosis can significantly impact patient treatment. Ultrasound imaging is often the initial diagnostic test for gallbladder cancer, making the enhancement of cancer detection accuracy from these images crucial. Despite the promising results of artificial intelligence techniques in disease diagnosis, their black-box nature hinders the reliability of their results and their practical application. Therefore, it is essential not to rely solely on a single model’s output and to further investigate for more reliable outcomes. This study presents a step-by-step structural investigation of forming an end-to-end model, a conjunction of two convolutional neural network based methods, for detecting gallbladder conditions. The final model, leveraging feature fusions and hierarchical classification, achieved a high accuracy of 92.62% for detecting normal, benign, and malignant gallbladders. It also achieved a remarkable accuracy of 98.36% for classifying normal and non-normal instances and 92.22% for classifying benign and malignant cases. Finally, comprehensive post-processing investigations, including cross-validation, temperature scaling, and uncertainty estimation, along with error analysis, are conducted to gain more insights into the model’s output. Among these insights, the model demonstrated resilience of its results to active dropout and augmentation at the inference phase. Furthermore, when applied with Test-Time data Augmentation, uncertainty estimation methods have better distinguishability between the uncertainties of correctly and incorrectly classified instances, which provides additional information about the model’s output. The source code of experiments conducted in this study is available at https://github.com/SaraDadjouy/GBCRet . Artificial Intelligence Deep Learning Uncertainty Estimation Gallbladder Cancer Ultrasound Hierarchical Classification Feature Fusion Full Text Additional Declarations No competing interests reported. Supplementary Files Highlights4.docx graphicalAbstract.docx 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-5014269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360464412,"identity":"b9cac148-f95a-4ea4-b5d0-242457937ba3","order_by":0,"name":"Sara Dadjouy","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Dadjouy","suffix":""},{"id":360464414,"identity":"e06db36e-9f94-46bb-bcde-0a0b11333524","order_by":1,"name":"Hedieh Sajedi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACAwkgIcHAzMMPFeAhXotkAzMpWhgYmBkMDjAT6TBz6faHHywqrGWMb+QfYPhRwyBj3kBAi+WcM8YSEmfSecxuJDMw9hxj4JE5QMhhN3IYJCTbDoO1MPA2MPBIEHKYwY30xz8k/x3mMZ4BtOUvcVoSzCQkGw7zGEgkMzATaUuOmYXEsXQeiTOPDQ7LHJMgzmG3JWqs7fnbEx8+fFNjY09QCwgww1QdAMUqUYDxA3HqRsEoGAWjYKQCAN1/NXgrivoEAAAAAElFTkSuQmCC","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Hedieh","middleName":"","lastName":"Sajedi","suffix":""}],"badges":[],"createdAt":"2024-09-01 20:06:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5014269/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5014269/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100796406,"identity":"d843d889-1859-440c-b422-7ad328f003e4","added_by":"auto","created_at":"2026-01-21 13:43:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1826604,"visible":true,"origin":"","legend":"","description":"","filename":"GallbladderCancer14030431.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5014269/v1_covered_0bd16aaf-ce44-42c6-ac87-eece0c11a7ff.pdf"},{"id":65893952,"identity":"077f2947-b5a8-4d4b-9647-92d8ec406bcd","added_by":"auto","created_at":"2024-10-04 06:04:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14177,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5014269/v1/5e6ea93d2c660793d98cc105.docx"},{"id":65893953,"identity":"3b8ba37e-0f5d-48cd-a8da-ebf0bc91737c","added_by":"auto","created_at":"2024-10-04 06:04:43","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":129038,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-5014269/v1/8faa98cf98ba0d0de2db4534.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gallbladder Cancer Detection via Ultrasound Image Analysis: An End-to-End Hierarchical Feature-Fused Model","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":"Artificial Intelligence, Deep Learning, Uncertainty Estimation, Gallbladder Cancer, Ultrasound, Hierarchical Classification, Feature Fusion","lastPublishedDoi":"10.21203/rs.3.rs-5014269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5014269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGallbladder cancer is a fatal disease, and its early diagnosis can significantly impact patient treatment. Ultrasound imaging is often the initial diagnostic test for gallbladder cancer, making the enhancement of cancer detection accuracy from these images crucial. Despite the promising results of artificial intelligence techniques in disease diagnosis, their black-box nature hinders the reliability of their results and their practical application. Therefore, it is essential not to rely solely on a single model\u0026rsquo;s output and to further investigate for more reliable outcomes. This study presents a step-by-step structural investigation of forming an end-to-end model, a conjunction of two convolutional neural network based methods, for detecting gallbladder conditions. The final model, leveraging feature fusions and hierarchical classification, achieved a high accuracy of 92.62% for detecting normal, benign, and malignant gallbladders. 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The source code of experiments conducted in this study is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SaraDadjouy/GBCRet\u003c/span\u003e\u003cspan address=\"https://github.com/SaraDadjouy/GBCRet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Gallbladder Cancer Detection via Ultrasound Image Analysis: An End-to-End Hierarchical Feature-Fused Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-04 06:04:38","doi":"10.21203/rs.3.rs-5014269/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":"b9c8c564-6161-4da4-9b41-5c065cb2435c","owner":[],"postedDate":"October 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T00:56:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-04 06:04:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5014269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5014269","identity":"rs-5014269","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}