A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection | 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 Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection Anam Iqbal, Shaima Qureshi, Mohammad Ahsan Chishti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4184652/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Cervical cancer is the fourth highest cause of death among women, but in its early stages, it shows no symptoms. A lack of effective early diagnosis is the fundamental cause of the disease's prevalence and poses the greatest obstacle to researchers. Pap smears, human papillomavirus tests, colposcopy, and biopsies are all invasive procedures that necessitate the assistance of medical professionals; they are time-consuming and do not identify cancer until a later stage. Thus, the absence of early symptoms and lack of invasive diagnostics, highlight the need for utilizing machine learning and deep learning approaches to revolutionize cervical cancer detection. To address this, we propose GCFS-CC(Genetic Algorithm based Context-aware Feature Selection for Cervical Cancer), which is a framework to detect cervical cancer from Pap smear images of cervical cells and classify these using a deep learning method. In our work, we use a genetic algorithm which is a bio-inspired algorithm, to select the best features from the acquired images. For feature selection, we incorporate the context-aware entropy gain method. We evaluated our model on one data set which combined a total of 917 normal and cancerous images, divided into seven classes. Using the selected features we build a deep learning model to validate our proposed framework. The results showed that after feature selection, the performance metrics(accuracy, precision, and recall), all improved. These results pave the way for GCFS-CC to be a valuable tool for early cervical cancer detection. This, however, comes with certain challenges, availability of high-quality images and proper parameter tuning for the genetic algorithm required for feature selection. To successfully implement machine learning-based cervical cancer detection into clinical practice, it will be necessary to address these challenges. Furthermore, any ethical and privacy concerns associated with the use of machine learning algorithms in healthcare settings must be addressed. cervical cancer pap smear machine learning deep learning bio-inspired feature selection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 05 Apr, 2024 Submission checks completed at journal 05 Apr, 2024 First submitted to journal 28 Mar, 2024 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. 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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-4184652","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287933413,"identity":"369981f0-1dd6-4505-a108-81d5f0fa3a5d","order_by":0,"name":"Anam Iqbal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYNACgxoGA4bExgcfGCSAPMYGIrRUHANqSW42nMEgIQHVYkBAyxlmoJL0NmkeBrA1DHi18M9ufrqBsY0tz5w9sU3adodFnfzsww3MBRV/cGqRuHPM7AZjm0yxZc/DZuvcMxISBucSG5hnnMHjsBsJIC1siRtuJDbezm0DauFhbGDmbcOtRf5G+jegFmaQlgZpS6AW+R6Qln+4tRjcyDG7AfQ+SEuTNCNQC8MZkJYG3FoMb+SU3QAGcrHBmYfNhr1tEpIbgFoO8xwzxqlF7kb6thvAqMwzOJ7+8MHPtjp++R72h495auRwex8ImIEBmoAicgCveihIIKhiFIyCUTAKRi4AAKY5VxYb/NcKAAAAAElFTkSuQmCC","orcid":"","institution":"NIT, Srinagar","correspondingAuthor":true,"prefix":"","firstName":"Anam","middleName":"","lastName":"Iqbal","suffix":""},{"id":287933414,"identity":"3d818c39-29f2-4d4b-8c01-c28bd09cacd7","order_by":1,"name":"Shaima Qureshi","email":"","orcid":"","institution":"NIT, Srinagar","correspondingAuthor":false,"prefix":"","firstName":"Shaima","middleName":"","lastName":"Qureshi","suffix":""},{"id":287933415,"identity":"1b7132ff-a74e-4fa0-847a-f92e603a98c9","order_by":2,"name":"Mohammad Ahsan Chishti","email":"","orcid":"","institution":"NIT, Srinagar","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Ahsan","lastName":"Chishti","suffix":""}],"badges":[],"createdAt":"2024-03-28 22:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4184652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4184652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54413287,"identity":"c2a9a375-4742-4f05-934a-2b91de903bd2","added_by":"auto","created_at":"2024-04-10 06:12:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":565684,"visible":true,"origin":"","legend":"","description":"","filename":"Cervicalcanceramended.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4184652/v1_covered_6e128514-70d4-4714-a698-11ad570133b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"numerical-algorithms","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"numa","sideBox":"Learn more about [Numerical Algorithms](http://link.springer.com/journal/11075)","snPcode":"11075","submissionUrl":"https://submission.nature.com/new-submission/11075/3","title":"Numerical Algorithms","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cervical cancer, pap smear, machine learning, deep learning, bio-inspired, feature selection","lastPublishedDoi":"10.21203/rs.3.rs-4184652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4184652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cervical cancer is the fourth highest cause of death among women, but in its early stages, it shows no symptoms. 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