GallNet: An Explainable Deep Learning Model for Multi-Class Gallbladder Disease Classification Using Ultrasound Imaging

GallNet: An Explainable Deep Learning Model for Multi-Class Gallbladder Disease Classification Using Ultrasound Imaging | 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 GallNet: An Explainable Deep Learning Model for Multi-Class Gallbladder Disease Classification Using Ultrasound Imaging Md.Faishal Ahmed Rudro, Afiah Rahman, Zannatul Ferdus, Nur Nabi Rahman, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7625399/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 Accurate and interpretable gallbladder disease categorization from ultrasound images is of prime importance for timely intervention as well as appropriate clinical decision-making in hepatobiliary practice. Current deep learning methods typically fail to cope with low class diversity, morphological heterogeneity, and opaqueness. We introduce GallNet, a thoughtfully constructed, memory-conscious convolutional neural network (CNN) for multi-class classification of gallbladder disease into nine clinically relevant classes with membranous/gangrenous cholecystitis, gallbladder perforation, and carcinoma as its milder and more severe manifestations. The hierarchical four-block architecture of GallNet (filters 32–256) learns dense hierarchies of features, while adaptive batch normalization and dropout aid in generalizability. To mitigate extreme class imbalance, we incorporate a class-weighted sparse categorical cross-entropy loss function. To the best of our knowledge, this is the first study to integrate both Grad-CAM and SHAP for explainable multi-class classification of gallbladder diseases from ultrasound images, offering complementary spatial and feature-level insights. We also point out reproducibility and stability, training on five independent runs with variant random seeds and reporting mean ± standard deviation of all the major performance metrics. Testing on a real-world diverse ultrasound dataset provides a test accuracy of 98.50 ± 0.05%, per-class precision, recall, and F1-scores > 96%, and class-wise AUCs ≥ 0.995. GallNet outperforms current state-of-the-art CNN models and demonstrates real-world viability as an efficient, transparent, and computationally smart gallbladder disease diagnostic aiding system. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Gastroenterology Physical sciences/Mathematics and computing Gallbladder Disease Classification Medical Image Analysis Deep Learning CNN Architecture Explainable AI Clinical Decision Support Grad-CAM SHAP 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-7625399","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518731859,"identity":"d4d036b1-591e-44f3-8c77-a7edf06a5472","order_by":0,"name":"Md.Faishal Ahmed 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Current deep learning methods typically fail to cope with low class diversity, morphological heterogeneity, and opaqueness. We introduce GallNet, a thoughtfully constructed, memory-conscious convolutional neural network (CNN) for multi-class classification of gallbladder disease into nine clinically relevant classes with membranous/gangrenous cholecystitis, gallbladder perforation, and carcinoma as its milder and more severe manifestations. The hierarchical four-block architecture of GallNet (filters 32\u0026ndash;256) learns dense hierarchies of features, while adaptive batch normalization and dropout aid in generalizability. To mitigate extreme class imbalance, we incorporate a class-weighted sparse categorical cross-entropy loss function. To the best of our knowledge, this is the first study to integrate both Grad-CAM and SHAP for explainable multi-class classification of gallbladder diseases from ultrasound images, offering complementary spatial and feature-level insights. 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GallNet outperforms current state-of-the-art CNN models and demonstrates real-world viability as an efficient, transparent, and computationally smart gallbladder disease diagnostic aiding system.\u003c/p\u003e","manuscriptTitle":"GallNet: An Explainable Deep Learning Model for Multi-Class Gallbladder Disease Classification Using Ultrasound Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 10:19:37","doi":"10.21203/rs.3.rs-7625399/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":"adaf2cf4-093a-4083-8c37-faaec1024d03","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55121228,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55121229,"name":"Health sciences/Diseases"},{"id":55121230,"name":"Health sciences/Gastroenterology"},{"id":55121231,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-10-05T12:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 10:19:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7625399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7625399","identity":"rs-7625399","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}