Advancing Radiological Dermatology with an Optimized Ensemble Deep Learning Model for Skin Lesion Classification

Advancing Radiological Dermatology with an Optimized Ensemble Deep Learning Model for Skin Lesion 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 Article Advancing Radiological Dermatology with an Optimized Ensemble Deep Learning Model for Skin Lesion Classification Adeel Akram, Tallha Akram, Ghada Atteia, Sultan Alanzai, Ayman Qahmash, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6214344/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 Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score in melanoma detection. The proposed method achieved classification accuracy of 98.5% evaluated using LSVM classifier on the Argentina Skin Lesion dataset underscoring the robustness of the proposed methodology. The proposed approach provides a scalable and computational efficient solution for automated skin lesion classification- contributing to improved clinical decision-making and enhanced patient outcomes. By aligning artificial intelligence with radiation-based medical imaging and bioinformatics, this research advances dermatological computer-aided diagnosis (CAD) systems, minimizing misclassification rates and supporting early skin cancer detection. The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis, contributing to improved clinical decision-making and enhanced patient outcomes. Biological sciences/Cancer Health sciences/Medical research Physical sciences/Mathematics and computing 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-6214344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443011366,"identity":"8741ec2a-53b4-46a6-b49f-397fa4f0a39e","order_by":0,"name":"Adeel Akram","email":"","orcid":"","institution":"COMSATS University Islamabad","correspondingAuthor":false,"prefix":"","firstName":"Adeel","middleName":"","lastName":"Akram","suffix":""},{"id":443011367,"identity":"f21d52f2-539f-41ed-a25b-82df205dad45","order_by":1,"name":"Tallha 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