A Design of End-to-End CNN Architecture for Colon Cancer Detection and Visualization Using ContinuousData Cleaning Method

A Design of End-to-End CNN Architecture for Colon Cancer Detection and Visualization Using ContinuousData Cleaning Method | 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 Article A Design of End-to-End CNN Architecture for Colon Cancer Detection and Visualization Using ContinuousData Cleaning Method Jie Li, Weiwei Goh, Noor Zaman Jhanjhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6533575/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 Colon cancer threatens the quality of life and causes deaths of mankind worldwide today. Convolutional Neural Networks(CNNs) are recognized as a robust and prevalent technique in deep learning methodologies for biomedical signals, includinghistopathology imaging and processing. As a diagnostic tool, medical CNNs gradually facilitate the decision-making process inclinical detection. However, there is still potential improvement for an accurate model to achieve millionth-level accuracy ofconfirmation. Beyond that, overfitting misleads performance of a model to properly recognize pattern especially to unseendata. Further, visualized result could facilitate interpretability of a detected results from clinical end.. This research aims tointroduce an end-to-end colon cancer detection using CNN with continuous parametric data cleaning method and interpretativevisualization approaches.Based on the results of the alternative settings, the raw dataset used in Experiment 1 caused overfitting during the trainingprocess, while Experiments 2 and 3 overcame the overfitting after applying Gaussian 95% and 99% rules based on RGBcolor distribution result. Further, there were 10 extensive experiments conducted based on settings of Experiment 3 to ensurethe reliability of the results, which showed 0.99996 averaged accuracy and 0.00044 averaged loss with 1,00 precision, recall,and F1-score. Lastly, a K-Mean clustering and 3D intensity visualized sample imagefor interpretability improvement in clinical diagnostic process. The comparison result indicated the proposedmodel gained superior accuracy compared to the benchmark studies from the past 5 years. This research contributed to anend-to-end CNN model in an accurate colon cancer detection Health sciences/Diseases/Cancer/Cancer imaging Health sciences/Diseases/Cancer/Cancer models 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-6533575","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":516977063,"identity":"59400ccb-23f4-4bd9-b2b3-f224e7a6e07b","order_by":0,"name":"Jie Li","email":"","orcid":"","institution":"Taylor's University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""},{"id":516977064,"identity":"a0b6e456-afff-49af-addc-3474f3c87c40","order_by":1,"name":"Weiwei Goh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFACxgYGBgMgLcF8AEhaQAR5iNPClghkSRCjBQYkeAyJ02JwvLlN4kMBg12/dM/3Bz/bJOTM+w8wPnjbxhBtcACHljMH2yRnGDAkz5xzdmNjb5uEscyNBGbDuW0MuRtwaDG7kdh2mweoxeBG7sYG3jaJxBkSDGzSvPi03H/YdvsPWEvOw8a/IC38B9h/49Vyg7HtNjDE7IBaGJvBtjAksDHj02J/JrH9Z4+BRILkjDTD2TLnJIwlJBKbJeeck8idiUOLZPvxxwY//tjY80skP/j4psxGToL/8MEPQEZuHw4tUCABikZgtLKBSRBbgkEBvxYGewj1B0lIvgG/llEwCkbBKBgxAABbyWAWaUJblgAAAABJRU5ErkJggg==","orcid":"","institution":"Taylor's University","correspondingAuthor":true,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Goh","suffix":""},{"id":516977065,"identity":"b2b08640-ebff-43d2-bd0b-91a6f8d6e21b","order_by":2,"name":"Noor Zaman Jhanjhi","email":"","orcid":"","institution":"Taylor's University","correspondingAuthor":false,"prefix":"","firstName":"Noor","middleName":"Zaman","lastName":"Jhanjhi","suffix":""}],"badges":[],"createdAt":"2025-04-26 08:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6533575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6533575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93109552,"identity":"3fceadf9-b1f6-4e15-b40f-9503126eb141","added_by":"auto","created_at":"2025-10-09 07:24:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2897763,"visible":true,"origin":"","legend":"","description":"","filename":"SRNew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6533575/v1_covered_b811b406-b337-460d-b6c6-4eba0e3d2667.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Design of End-to-End CNN Architecture for Colon Cancer Detection and Visualization Using ContinuousData Cleaning Method","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6533575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6533575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Colon cancer threatens the quality of life and causes deaths of mankind worldwide today. Convolutional Neural Networks(CNNs) are recognized as a robust and prevalent technique in deep learning methodologies for biomedical signals, includinghistopathology imaging and processing. As a diagnostic tool, medical CNNs gradually facilitate the decision-making process inclinical detection. However, there is still potential improvement for an accurate model to achieve millionth-level accuracy ofconfirmation. Beyond that, overfitting misleads performance of a model to properly recognize pattern especially to unseendata. Further, visualized result could facilitate interpretability of a detected results from clinical end.. This research aims tointroduce an end-to-end colon cancer detection using CNN with continuous parametric data cleaning method and interpretativevisualization approaches.Based on the results of the alternative settings, the raw dataset used in Experiment 1 caused overfitting during the trainingprocess, while Experiments 2 and 3 overcame the overfitting after applying Gaussian 95% and 99% rules based on RGBcolor distribution result. Further, there were 10 extensive experiments conducted based on settings of Experiment 3 to ensurethe reliability of the results, which showed 0.99996 averaged accuracy and 0.00044 averaged loss with 1,00 precision, recall,and F1-score. Lastly, a K-Mean clustering and 3D intensity visualized sample imagefor interpretability improvement in clinical diagnostic process. The comparison result indicated the proposedmodel gained superior accuracy compared to the benchmark studies from the past 5 years. 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