CHASHNIt: Enhancing Skin Disease Classificationleveraging GAN-Augmented Hybrid Model withComparative XAI Interpretation Heatmap Using LIMEand SHAP Algorithms

CHASHNIt: Enhancing Skin Disease Classificationleveraging GAN-Augmented Hybrid Model withComparative XAI Interpretation Heatmap Using LIMEand SHAP Algorithms | 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 CHASHNIt: Enhancing Skin Disease Classificationleveraging GAN-Augmented Hybrid Model withComparative XAI Interpretation Heatmap Using LIMEand SHAP Algorithms Saksham Anand, Abhiram Sharma, B Natarajan, Ashwin Singh Slathia, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6460989/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Correct categorization of skin diseases is vital for prompt diagnosis. However, obstacles such as imbalance of data andinterpretability of deep learning models limit their use in medical settings. To overcome these setbacks, Combined Hybrid Architecture for Scalable High-performance in Neural Iterations or CHASHNIt is proposed, which is an integration of EfficientNetB7,DenseNet201, and InceptionResNetV2 to outperform current models on every ground. GAN-based data augmentation is usedto create synthetic images, to ensure that all classes are equally represented. Sophisticated preprocessing methods such asnormalization and feature selection improve data quality and model generalization. Explainable AI methods, i.e., SHAP andLIME, enable model decision-making transparent. A rigorous comparative analysis testifies to the excellence of CHASHNItcompared to other benchmark models with 97.8% accuracy, 98.1% precision, 97.5% recall, 97.6% F1 Score and IoU of 92.3%,which exceeds Swin Transformer, ResNet101, InceptionResNetV2, MobileNetV3, EfficientNetB7, DenseNet201, and ConvNeXtmodels. The model was trained and tested on a 19,500-image dataset of 23 types of skin diseases with 80:20 split for trainingand testing. An ablation study testifies to the synergy advantage of the hybrid approach. LIME-SHAP heatmaps confirm themodel’s predictive result. CHASHNIt is a revolutionary leap in the classification of skin diseases, attaining a balance betweenscalability, accuracy, and explainability. Computational complexity is the sole drawback, but future developments will optimizeefficiency for low-resource devices. Health sciences/Medical research/Translational research Biological sciences/Computational biology and bioinformatics/Classification and taxonomy Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Image processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 31 May, 2025 Reviews received at journal 26 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers invited by journal 21 May, 2025 Editor assigned by journal 21 May, 2025 Editor invited by journal 24 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 16 Apr, 2025 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. 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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-6460989","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":460350502,"identity":"4637bcee-88cb-4f64-933d-1cdb7edfe03a","order_by":0,"name":"Saksham Anand","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Saksham","middleName":"","lastName":"Anand","suffix":""},{"id":460350503,"identity":"43a07fd0-2d64-44f2-be05-9a1c63f26ee5","order_by":1,"name":"Abhiram Sharma","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Abhiram","middleName":"","lastName":"Sharma","suffix":""},{"id":460350504,"identity":"b01b11a9-bccf-4d4d-9e7a-ea8530b2d281","order_by":2,"name":"B Natarajan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3RPQrCMBTA8VcKzyXWtV3qFSKC0kG8iiXgrJugQ1x0Ueceo6tbJVCXQteKi9ALFARBELHBjzF1FMwfMgTejyQEQKf73bBcxDgBfW4j9bT5ISYFSr8mMoI2vI9R1a2lu/NkZjW7UI+nZHSDxjIyxEhBvBUznSTG1pZbwyMpL2YnAxCBgtCIgcMRjTAinUMg35IBCKIiaW5e+R37kowlaVaSjKEzX6AvCRQlodUk73jzDbJQWMwuaJu0Ep9XXMzPD/wS98L9elcMbq7r7oU4q8ir+PM/5bDBqwHA7JshnU6n+9ceqS1IIN3uYoUAAAAASUVORK5CYII=","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"B","middleName":"","lastName":"Natarajan","suffix":""},{"id":460350505,"identity":"b8c13d27-b454-4e3c-ae24-db0fdefb08c8","order_by":3,"name":"Ashwin Singh Slathia","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Ashwin","middleName":"Singh","lastName":"Slathia","suffix":""},{"id":460350506,"identity":"f1cde256-04aa-4308-84e3-0ad546f83aef","order_by":4,"name":"Ayush Rathi","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Ayush","middleName":"","lastName":"Rathi","suffix":""},{"id":460350507,"identity":"0aafe578-4b9f-4159-8950-81705b89adb1","order_by":5,"name":"Krishna Priyadarshan Behara","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"Priyadarshan","lastName":"Behara","suffix":""},{"id":460350508,"identity":"80e1cf1a-05e5-4edd-a44c-b80d18885f85","order_by":6,"name":"R Elakkiya","email":"","orcid":"","institution":"Birla Institute of Technology and Science, Pilani - 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However, obstacles such as imbalance of data andinterpretability of deep learning models limit their use in medical settings. To overcome these setbacks, Combined Hybrid Architecture for Scalable High-performance in Neural Iterations or CHASHNIt is proposed, which is an integration of EfficientNetB7,DenseNet201, and InceptionResNetV2 to outperform current models on every ground. GAN-based data augmentation is usedto create synthetic images, to ensure that all classes are equally represented. Sophisticated preprocessing methods such asnormalization and feature selection improve data quality and model generalization. Explainable AI methods, i.e., SHAP andLIME, enable model decision-making transparent. 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