D-GAN: An Automatic Acne Detection, Severity, and Assessment Framework using Generative Adversarial Network with Deep Neural Network

D-GAN: An Automatic Acne Detection, Severity, and Assessment Framework using Generative Adversarial Network with Deep Neural Network | 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 D-GAN: An Automatic Acne Detection, Severity, and Assessment Framework using Generative Adversarial Network with Deep Neural Network Umara Khalid, Li Chen, Abdullah Ayub Khan, Faisal Mehmood This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3957770/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 With the robust development of Artificial Intelligence (AI), especially image processing has made information technology more efficient and effective in the sense to evaluate facial features, even though there has been masked on the face. However, the accuracy of acne detection and related severity analysis is becoming a significant prospect for the precise treatment of patients. Due to this, close severity is one of the features that need to be added first, while it is considered a highly challenging aspect for dermatologists because the similar appearance of acne in the face reduces the rate of accuracy when examining. It poses a serious problem in the domain of biomedical processing and controls. In this paper, we contribute to four different folds. Initially, this paper presents a novel framework that provides a platform in order to measure localization and segmentation. In this process, consultants receive better accuracy and efficiency during the process of acne detection and severity analysis. Second, this paper utilizes deep neural networks (DNNs) as a backend process to lightning the extraction of multi-scale features through a multi-hierarchy neural net for regionalized facial features to investigate distinction and localization. Third, a class-based segmentation approach customizes and integrates with the proposed framework to examine the background and facial skin separation to distinguish different classes to obtain severity marking. Fourth, the facial skin segmentation classes are built as a cluster segment using Generative Adversarial Network (GAN). It provides a high-resolution network that enhances severity masking awareness and related attentions, including shuffle and conditional channels like an update in weight block. Although, the simulation illustrates that the technological collaboration achieves promising results and elaborates on the accuracy and efficiency while the detection of acne as compared to other state-of-the-art baseline methods. In addition, the proposed framework illustrates good results in severity analysis for acne detection comparably far better than the previously published articles with a performance rate of 3.112% (accuracy), 1.131% (segmentation), 2.317% (localization), and 1.573% (GAN-based high-resolution network management), respectively. Face Acne Detection Severity Analysis Artificial Intelligence Generative Adversarial Network Deep Neural Network Class-based Segmentation 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-3957770","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273012620,"identity":"80dd5d5b-fa73-4068-9f3f-04342a10b1e6","order_by":0,"name":"Umara Khalid","email":"data:image/png;base64,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","orcid":"","institution":"Northwest University","correspondingAuthor":true,"prefix":"","firstName":"Umara","middleName":"","lastName":"Khalid","suffix":""},{"id":273012621,"identity":"8e495b4e-1c49-4629-b04d-92720e3e8ad7","order_by":1,"name":"Li Chen","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Chen","suffix":""},{"id":273012622,"identity":"2f9b75dc-5ac3-4b2a-be27-018df6905e62","order_by":2,"name":"Abdullah Ayub Khan","email":"","orcid":"","institution":"Sindh Madressatul Islam University","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"Ayub","lastName":"Khan","suffix":""},{"id":273012623,"identity":"2b94c9f5-8ba6-481d-9593-0fe62a658866","order_by":3,"name":"Faisal Mehmood","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Mehmood","suffix":""}],"badges":[],"createdAt":"2024-02-15 06:17:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3957770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3957770/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51764254,"identity":"7001b0e4-2230-4ee3-8b9b-25d1a6301ea4","added_by":"auto","created_at":"2024-02-28 17:55:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":759513,"visible":true,"origin":"","legend":"","description":"","filename":"DGANAnAutomaticAcneDetectionSeverityandAssessmentFrameworkusingGenerativeAdversarialNetworkwithDeepNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3957770/v1_covered_8b3d5cc2-8c3b-4de0-bf48-c9febd89b9e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"D-GAN: An Automatic Acne Detection, Severity, and Assessment Framework using Generative Adversarial Network with Deep Neural Network","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":"Face Acne Detection, Severity Analysis, Artificial Intelligence, Generative Adversarial Network, Deep Neural Network, Class-based Segmentation","lastPublishedDoi":"10.21203/rs.3.rs-3957770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3957770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the robust development of Artificial Intelligence (AI), especially image processing has made information technology more efficient and effective in the sense to evaluate facial features, even though there has been masked on the face. 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