Investigation on Effects of Training Schemes and Data Characteristics on Deep Learning-based Breast Cancer Classification

Investigation on Effects of Training Schemes and Data Characteristics on Deep Learning-based Breast Cancer Classification | 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 Investigation on Effects of Training Schemes and Data Characteristics on Deep Learning-based Breast Cancer Classification Madhumita Pal, Smita Parija, Ganapati Panda, Adysha Rath, Sujata Dash, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4227014/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 (BC) is the most frequently diagnosed cancer among women, surpassing all other types of cancer in terms of prevalence. It affects both males and females, but women are at a greater risk of developing it. The lifetime probability of developing breast cancer for women is approximately 1 in 38. The focus of this study is to differentiate between benign and malignant breast cancer tumors using the fine needle aspiration (FNA) signal as the primary source of information. Four deep learning (DL) models, namely long short-term memory (LSTM), Gated recurrent unit (GRU), Deep belief network (DBN), and autoencoder (AE) have been utilized to achieve this goal. The proposed models have been trained and validated using two public breast cancer datasets: the Wisconsin Original Breast Cancer dataset (WBC) and the Wisconsin Diagnostic Breast Cancer dataset (WDBC). To establish a reliable model, three different types of training techniques have been utilized, including the 80:20 split, the 70:30 split, and the k-fold method. The experimental investigation incorporated three different data characteristics, namely balanced, less imbalanced, and extremely imbalanced data. The simulation-based experimental findings indicate that the LSTM model achieves high levels of accuracy, F1-score, and area under the curve (AUC) when applied to the two commonly used datasets. The WDBC dataset yields accuracy, F1-score, and AUC values of 0.98, 0.98, and 0.99, respectively, while the WBCD dataset yields values of 0.99, 0.99, and 1, respectively. These results were obtained using a 3-fold training scheme and balanced data. The LSTM model consistently outperforms the other three models, regardless of variations in datasets, training methods, and changes in data properties. The efficacy of the models can be evaluated by subjecting the deep learning models to bigger and varying degrees of unbalanced data samples, including both balanced and less skewed datasets. To further this study, we aim to explore the effectiveness of DL models in conjunction with an IoT system to improve breast cancer detection accuracy in online mode for patients residing in remote areas. Breast cancer DL models DL-based BC classification balanced data imbalanced data 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-4227014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289532204,"identity":"4a44b6b0-aa31-4ad3-a675-d27f711d873b","order_by":0,"name":"Madhumita Pal","email":"","orcid":"","institution":"C. V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Madhumita","middleName":"","lastName":"Pal","suffix":""},{"id":289532205,"identity":"6edd2a3f-f522-45e7-b6d9-ebafe532e3f7","order_by":1,"name":"Smita Parija","email":"","orcid":"","institution":"C. V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Smita","middleName":"","lastName":"Parija","suffix":""},{"id":289532206,"identity":"7fa9d675-75b0-416b-a670-7f2a6147e574","order_by":2,"name":"Ganapati Panda","email":"","orcid":"","institution":"C. V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Ganapati","middleName":"","lastName":"Panda","suffix":""},{"id":289532207,"identity":"596a64a3-d623-4c22-8469-6b0ab1a8b73a","order_by":3,"name":"Adysha Rath","email":"","orcid":"","institution":"C. V. 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It affects both males and females, but women are at a greater risk of developing it. The lifetime probability of developing breast cancer for women is approximately 1 in 38. The focus of this study is to differentiate between benign and malignant breast cancer tumors using the fine needle aspiration (FNA) signal as the primary source of information. Four deep learning (DL) models, namely long short-term memory (LSTM), Gated recurrent unit (GRU), Deep belief network (DBN), and autoencoder (AE) have been utilized to achieve this goal. The proposed models have been trained and validated using two public breast cancer datasets: the Wisconsin Original Breast Cancer dataset (WBC) and the Wisconsin Diagnostic Breast Cancer dataset (WDBC). To establish a reliable model, three different types of training techniques have been utilized, including the 80:20 split, the 70:30 split, and the k-fold method. The experimental investigation incorporated three different data characteristics, namely balanced, less imbalanced, and extremely imbalanced data. The simulation-based experimental findings indicate that the LSTM model achieves high levels of accuracy, F1-score, and area under the curve (AUC) when applied to the two commonly used datasets. The WDBC dataset yields accuracy, F1-score, and AUC values of 0.98, 0.98, and 0.99, respectively, while the WBCD dataset yields values of 0.99, 0.99, and 1, respectively. These results were obtained using a 3-fold training scheme and balanced data. The LSTM model consistently outperforms the other three models, regardless of variations in datasets, training methods, and changes in data properties. The efficacy of the models can be evaluated by subjecting the deep learning models to bigger and varying degrees of unbalanced data samples, including both balanced and less skewed datasets. To further this study, we aim to explore the effectiveness of DL models in conjunction with an IoT system to improve breast cancer detection accuracy in online mode for patients residing in remote areas.\u003c/p\u003e","manuscriptTitle":"Investigation on Effects of Training Schemes and Data Characteristics on Deep Learning-based Breast Cancer Classification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 03:06:02","doi":"10.21203/rs.3.rs-4227014/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":"6dacc742-c00a-43a0-9a8d-9c5cbe6607ad","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T13:12:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 03:06:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4227014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4227014","identity":"rs-4227014","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}