A Machine Learning Validation for Identifying the difference between Benign Cysts and Malignant Tumor in Breast Cancer

A Machine Learning Validation for Identifying the difference between Benign Cysts and Malignant Tumor in Breast Cancer | 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 Machine Learning Validation for Identifying the difference between Benign Cysts and Malignant Tumor in Breast Cancer Akshara Makrariya, Rabia Musheer Aziz, Ankita Saha, Korhan Cengiz, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7167668/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 Breast cancer is a leading health concern for women globally. Hormonal fluctuations can lead to benign breast abnormalities, such as cysts, such as cysts, which may have potential to become cancerous. Researchers have been proposed various thermal modelling techniques to detect breast cancer by differentiating between healthy and cancerous breast tissues. There is a gap literature regarding the use of thermal models to differentiate between benign and malignant breast disorders in women. This research focuses on the thermal changes caused by benign and malignant breast tumor in fatty tissue of woman’s breast. Experiment was conducted in School of Advanced Sciences and languages, VIT Bhopal University, India. That was approved by King Khalid University, and University of Arizona. A 2D steady-state model was developed using Penne’s bioheat equation, incorporating ket parameters such as blood flow rate, thermal conductivity and metabolic heat generation. The model incorporates adiabatic boundary conditions were defined for different environmental conditions. Solutions were computed using finite element analysis. Simulations yielded results for various spherical cysts and different tissue depths within a hemispherical breast model. Furthermore, this study compared thermal profiles associated with cysts and malignant tumors breast tissue. This study reveals distinct thermal differences between cyst and malignant tumour in breast tissue, potentially aiding in accurate diagnosis and reducing false-positive. This study identified several factors influencing cancer classification and prediction accuracy. The data used here is publicly available on Mammographic Image Analysis Society (MIAS) database [ 35 ]. Cysts and malignant tumors exhibit opposite thermal effects in breast tissue. The curve’s slope at the cyst-normal tissue interface is opposite to that at the tumor-normal tissue interface. Thermography can leverage the distinct thermal signatures of cysts and tumors to detect and characterize these abnormalities in breast tissue. Thermography can accurately differentiate cysts and cancerous tumors, reducing false positives. Notably, this technique can detect small lump (2mm diameter/1mm radius) at depths of up to 2.5 cm. This study finds SVM outperforms RF and ANN in breast cancer classification, leveraging computationally extracted features (viz., avg. accuracies of SVM, ANN and RF are 96.66%, 93.88% and 90.55%, respectively). In this study, we classified breast cancer data using three machine learning techniques: Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF). We optimized model performance by training with selected features based on the analysis. Benign tumors Cysts Cancer Prediction Numerical analysis Thermal gradients Random Forest (R.F.) Support Vector Machine (SVM) Artificial Neural Network (ANN) 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-7167668","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498743347,"identity":"3f7dd722-3627-4b41-8b2a-ef04ffb2db4c","order_by":0,"name":"Akshara Makrariya","email":"","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":false,"prefix":"","firstName":"Akshara","middleName":"","lastName":"Makrariya","suffix":""},{"id":498743349,"identity":"73496103-334f-4998-a0dc-f5e080227be4","order_by":1,"name":"Rabia Musheer Aziz","email":"","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"Musheer","lastName":"Aziz","suffix":""},{"id":498743351,"identity":"02c75033-ca8e-435c-9dc7-ff34bbb68412","order_by":2,"name":"Ankita Saha","email":"","orcid":"","institution":"Swami Vivekananda University","correspondingAuthor":false,"prefix":"","firstName":"Ankita","middleName":"","lastName":"Saha","suffix":""},{"id":498743353,"identity":"01d6f3f0-938d-4a9a-a183-fd8d2eb33b8a","order_by":3,"name":"Korhan Cengiz","email":"","orcid":"","institution":"Prince Mohammad bin Fahd University","correspondingAuthor":false,"prefix":"","firstName":"Korhan","middleName":"","lastName":"Cengiz","suffix":""},{"id":498743355,"identity":"3f40db39-8c65-4d98-9758-9712338a2257","order_by":4,"name":"Mohd Asif Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCRBhICcHog48IEGLsTFYSwLxWhiMExtAFFFa+KWbH378UWCQPj/s8EOgLXZyug0EtEjOOWYszWNgkLvxdpoBUEuysdkBAloMbiQYSDMY/MndODsBpOVA4jbCWtI///xhYJBuODv9A7FacswkgA5LkJfOIdIWyRk5ZdZALYYbpHMKDiQYEOEXfon0zTd//DGQl5+dvvnDhwo7OYJaEC4EqzQgVjkIyDeQonoUjIJRMApGFAAA/FhDpQIRtLUAAAAASUVORK5CYII=","orcid":"","institution":"Bakhtar University","correspondingAuthor":true,"prefix":"","firstName":"Mohd","middleName":"Asif","lastName":"Shah","suffix":""},{"id":498743360,"identity":"f85614c5-e5f5-4150-b195-d8a8bfea6ae0","order_by":5,"name":"Saurav Mallik","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Saurav","middleName":"","lastName":"Mallik","suffix":""}],"badges":[],"createdAt":"2025-07-20 05:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7167668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7167668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90418770,"identity":"d6b32a27-2f8e-4844-871b-7450ab464fca","added_by":"auto","created_at":"2025-09-02 13:39:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":684637,"visible":true,"origin":"","legend":"","description":"","filename":"FinalmanuscriptAksharaMaamrabiaankitasmv5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7167668/v1_covered_f0624361-b77f-4b1a-b77b-3e4bb5ddab26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Machine Learning Validation for Identifying the difference between Benign Cysts and Malignant Tumor in Breast Cancer","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":"Benign tumors, Cysts, Cancer Prediction, Numerical analysis, Thermal gradients, Random Forest (R.F.), Support Vector Machine (SVM), Artificial Neural Network (ANN)","lastPublishedDoi":"10.21203/rs.3.rs-7167668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7167668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer is a leading health concern for women globally. Hormonal fluctuations can lead to benign breast abnormalities, such as cysts, such as cysts, which may have potential to become cancerous. Researchers have been proposed various thermal modelling techniques to detect breast cancer by differentiating between healthy and cancerous breast tissues. There is a gap literature regarding the use of thermal models to differentiate between benign and malignant breast disorders in women. This research focuses on the thermal changes caused by benign and malignant breast tumor in fatty tissue of woman\u0026rsquo;s breast. Experiment was conducted in School of Advanced Sciences and languages, VIT Bhopal University, India. That was approved by King Khalid University, and University of Arizona. A 2D steady-state model was developed using Penne\u0026rsquo;s bioheat equation, incorporating ket parameters such as blood flow rate, thermal conductivity and metabolic heat generation. The model incorporates adiabatic boundary conditions were defined for different environmental conditions. Solutions were computed using finite element analysis. Simulations yielded results for various spherical cysts and different tissue depths within a hemispherical breast model. Furthermore, this study compared thermal profiles associated with cysts and malignant tumors breast tissue. This study reveals distinct thermal differences between cyst and malignant tumour in breast tissue, potentially aiding in accurate diagnosis and reducing false-positive. This study identified several factors influencing cancer classification and prediction accuracy. The data used here is publicly available on Mammographic Image Analysis Society (MIAS) database [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Cysts and malignant tumors exhibit opposite thermal effects in breast tissue. The curve\u0026rsquo;s slope at the cyst-normal tissue interface is opposite to that at the tumor-normal tissue interface. Thermography can leverage the distinct thermal signatures of cysts and tumors to detect and characterize these abnormalities in breast tissue. Thermography can accurately differentiate cysts and cancerous tumors, reducing false positives. Notably, this technique can detect small lump (2mm diameter/1mm radius) at depths of up to 2.5 cm. This study finds SVM outperforms RF and ANN in breast cancer classification, leveraging computationally extracted features (viz., avg. accuracies of SVM, ANN and RF are 96.66%, 93.88% and 90.55%, respectively). In this study, we classified breast cancer data using three machine learning techniques: Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF). We optimized model performance by training with selected features based on the analysis.\u003c/p\u003e","manuscriptTitle":"A Machine Learning Validation for Identifying the difference between Benign Cysts and Malignant Tumor in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 11:52:04","doi":"10.21203/rs.3.rs-7167668/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":"df2d0d2d-19e0-4fca-9fa3-ad017a05cb34","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-02T13:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 11:52:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7167668","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7167668","identity":"rs-7167668","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}