Accurate Cell Classification in Neuronal Fluorescent Images: A Data Mining Approach

Accurate Cell Classification in Neuronal Fluorescent Images: A Data Mining Approach | 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 Accurate Cell Classification in Neuronal Fluorescent Images: A Data Mining Approach Raisa Akhtaruzzaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7707261/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Cellular morphology can be used to identify cytoskeletal structural integrity of cells and hence shape analysis of cells is of importance to research. A comprehensive framework was developed to classify cell shapes using predictive modeling and optimization techniques. By integrating circularity and ellipsodality data cells from fluorescent images were classified based on shape. Advanced machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks, were employed and optimized through hyperparameter tuning to enhance predictive accuracy. Sensitivity analysis was conducted to assess the impact of varying circularity and ellipsodality, while scenario testing validated the robustness of the framework under hypothetical conditions. The findings indicated that KNN other models, delivering superior accuracy and reliability. This study offers a scalable and adaptable methodology to support data-driven decision-making in cell structure prediction, addressing the pressing need for accurate cell analysis. 1. Background Studies on cellular morphology and phenotypic characteristics are integral to multidisciplinary research areas, including disease modeling and the mechanosensitive of biological samples. The analysis of cellular structure is crucial for understanding cell integrity and functionality, particularly in the context of neuroblastoma cells. In this study, fluorescence microscopy was used to collect data from SH-SY5Y neuroblastoma cells, which were stained using a live/dead assay kit (Ethidium Homodimer and Calcein AM). This methodology facilitates the assessment of cell viability and structural integrity, allowing for a comprehensive analysis of cell shapes. The importance of accurate cell shape classification extends beyond basic research; it has significant implications for clinical applications, including diagnostics and therapeutic strategies. The development of advanced predictive modeling techniques to classify cell shapes can enhance our understanding of cellular behavior and improve decision-making in research and clinical settings. Furthermore, the findings from sensitivity analysis and scenario testing validate the robustness of the proposed framework, reinforcing its potential as a scalable and adaptable tool for data-driven decision-making in cell analysis. Mechanical Engineering Thermodynamics and statistical mechanics Cellular Morphology Fluorescence Microscopy Machine Learning Predictive Modeling Neuroblastoma (SH-SY5Y) Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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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-7707261","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521059459,"identity":"a2fc7dda-50eb-411e-87fa-380bd5c047b0","order_by":0,"name":"Raisa Akhtaruzzaman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYHAC9h8fftjIIYsYENQjObMnzRhZNWEt0hxshxMbiNZiPiP5gTEDT1r62vYzxh8Y2/7IM7A3b5PAp0XmzDGD5AILm9xtZ3LMJBjbDAwbeI6V4dUiwd5gcHgGT1rutgNpaQxALYwNEkC9eLUws39s5mE7nG52/lky0GEG9g3ybwhoYe8xZgZqSTC7kXwA5LDEBgkeAlp4zpQxAgPZcNuNx8ckEs4ZJ7fxpBVb4NUikb6NARiV8mbnE5s/fCiTs+1nP7zxBj4tqCABiNmIVz4KRsEoGAWjABcAAJc3RY+/9BMaAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Raisa","middleName":"","lastName":"Akhtaruzzaman","suffix":""}],"badges":[],"createdAt":"2025-09-24 23:28:50","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7707261/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-7707261/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566125,"identity":"3fccea9f-15bc-42be-86c0-4261195ab681","added_by":"auto","created_at":"2026-03-27 12:55:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":911290,"visible":true,"origin":"","legend":"","description":"","filename":"PROJECTREPORTFINALraisa1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7707261/v2_covered_eb40d0a6-a977-4add-a92a-534d51e47179.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Accurate Cell Classification in Neuronal Fluorescent Images: A Data Mining Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Texas at Arlington","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":"Cellular Morphology, Fluorescence Microscopy, Machine Learning, Predictive Modeling, Neuroblastoma (SH-SY5Y)","lastPublishedDoi":"10.21203/rs.3.rs-7707261/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7707261/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCellular morphology can be used to identify cytoskeletal structural integrity of cells and hence shape analysis of cells is of importance to research. A comprehensive framework was developed to classify cell shapes using predictive modeling and optimization techniques. By integrating circularity and ellipsodality data cells from fluorescent images were classified based on shape. Advanced machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks, were employed and optimized through hyperparameter tuning to enhance predictive accuracy. Sensitivity analysis was conducted to assess the impact of varying circularity and ellipsodality, while scenario testing validated the robustness of the framework under hypothetical conditions. The findings indicated that KNN other models, delivering superior accuracy and reliability. This study offers a scalable and adaptable methodology to support data-driven decision-making in cell structure prediction, addressing the pressing need for accurate cell analysis.\u003c/p\u003e \u003cp\u003e1. Background\u003c/p\u003e \u003cp\u003eStudies on cellular morphology and phenotypic characteristics are integral to multidisciplinary research areas, including disease modeling and the mechanosensitive of biological samples. The analysis of cellular structure is crucial for understanding cell integrity and functionality, particularly in the context of neuroblastoma cells.\u003c/p\u003e \u003cp\u003eIn this study, fluorescence microscopy was used to collect data from SH-SY5Y neuroblastoma cells, which were stained using a live/dead assay kit (Ethidium Homodimer and Calcein AM). This methodology facilitates the assessment of cell viability and structural integrity, allowing for a comprehensive analysis of cell shapes.\u003c/p\u003e \u003cp\u003eThe importance of accurate cell shape classification extends beyond basic research; it has significant implications for clinical applications, including diagnostics and therapeutic strategies. The development of advanced predictive modeling techniques to classify cell shapes can enhance our understanding of cellular behavior and improve decision-making in research and clinical settings. 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