A Hybrid Advanced Machine Learning Stacked Model with Genetic Weighting for Accurate Breast Lesion Classification on Ultrasound Imaging

A Hybrid Advanced Machine Learning Stacked Model with Genetic Weighting for Accurate Breast Lesion Classification on 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 Research Article A Hybrid Advanced Machine Learning Stacked Model with Genetic Weighting for Accurate Breast Lesion Classification on Ultrasound Imaging Awatif M. Omer, Wael M.S. Yafooz, Sultan Abdulwadoud Alshoabi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8820190/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective This study aimed to develop and evaluate a hybrid advanced machine learning model in classification of breast lesion using ultrasound-derived features. Material and methods A retrospective study done with the compromise a total of 206 lesion dataset of breast ultrasound imaging was analyzed, with lesions classified according to the BI-RADS classification system. Several model algorisms were evaluated including conventional machine learning classifiers, including Logistic Regression, Support Vector Machine, Decision Tree, and Naïve Bayes, in addition to ensemble methods such as Random Forest and Gradient Boosting. A hybrid advanced machine learning stacked model with genetic weighting was also evaluated to enhance feature discrimination and robustness. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, and F1-score, to appropriately class imbalance. Result The model achieves high performance compared with conventional and machine learning models with an overall accuracy exceeding 90% and F1-score above 85%. also demonstrated an improved classification consistency across BI-RADS categories, achieving F1-scores of 81.25% and 68.97% for BI-RADS 2 and 3, respectively, and showed enhanced robustness in intermediate and high-risk categories despite class imbalance. Conclusion the study conclude that the proposed hybrid stacked machine learning model with genetic weighting provides a reliable and robust approach for breast lesion classification using ultrasound imaging. By effectively addressing class imbalance and improving discrimination across BI-RADS categories, the model shows strong potential as a supportive decision-making tool in clinical breast imaging. Breast Lesion Classification Genetic algorithm Deep learning Ultrasound Imaging Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Mar, 2026 Editor invited by journal 13 Feb, 2026 Editor assigned by journal 13 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 08 Feb, 2026 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-8820190","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602842231,"identity":"cc2b735d-6e68-47c9-a9ca-fffde2d0a783","order_by":0,"name":"Awatif M. 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Several model algorisms were evaluated including conventional machine learning classifiers, including Logistic Regression, Support Vector Machine, Decision Tree, and Na\u0026iuml;ve Bayes, in addition to ensemble methods such as Random Forest and Gradient Boosting. A hybrid advanced machine learning stacked model with genetic weighting was also evaluated to enhance feature discrimination and robustness. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, and F1-score, to appropriately class imbalance.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe model achieves high performance compared with conventional and machine learning models with an overall accuracy exceeding 90% and F1-score above 85%. also demonstrated an improved classification consistency across BI-RADS categories, achieving F1-scores of 81.25% and 68.97% for BI-RADS 2 and 3, respectively, and showed enhanced robustness in intermediate and high-risk categories despite class imbalance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ethe study conclude that the proposed hybrid stacked machine learning model with genetic weighting provides a reliable and robust approach for breast lesion classification using ultrasound imaging. By effectively addressing class imbalance and improving discrimination across BI-RADS categories, the model shows strong potential as a supportive decision-making tool in clinical breast imaging.\u003c/p\u003e","manuscriptTitle":"A Hybrid Advanced Machine Learning Stacked Model with Genetic Weighting for Accurate Breast Lesion Classification on Ultrasound Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 07:11:55","doi":"10.21203/rs.3.rs-8820190/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-06T07:31:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-13T17:11:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T11:21:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T11:20:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-02-08T08:24:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3dfadbc3-f2d5-4ea5-b6ce-2ad47713d14e","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T07:11:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 07:11:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8820190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8820190","identity":"rs-8820190","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}