A Lightweight Hybrid CNN Model for Classification of Arsenic-Induced Skin Lesions

A Lightweight Hybrid CNN Model for Classification of Arsenic-Induced Skin Lesions | 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 Lightweight Hybrid CNN Model for Classification of Arsenic-Induced Skin Lesions Tasnim Sultana Sintheia, Md. Sayem Kabir, Kazi Tanvir, Dipta Gomes, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6612143/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 Chronic arsenic poisoning, largely attributable to sustained intake of contaminated groundwater, represents a critical public health concern, particularly within South Asian populations. It leads to severe medical conditions such as skin lesions, cancer, and cardiovascular ailments. This study introduces an advanced deep learning framework designed to automatically detect and classify arsenic-induced skin lesions using images captured via smartphones in Bangladesh. The developed hybrid convolutional neural network (CNN) architecture combines parallel CNN pathways, Dense and Residual blocks for efficient feature extraction, and lightweight Fire modules to optimize computational performance. Preprocessing of images involved resizing, normalization, and specialized augmentation strategies to mitigate class imbalances and enhance the training efficacy. The model achieved outstanding accuracy of 98.33%, surpassing the performance of standard CNNs (DenseNet121, ResNet50V2) and other hybrid configurations. Detailed performance assessments confirmed robust predictive power, yielding precision of 0.9900, recall of 0.9867, F1-score of 0.9883, specificity of 0.9867, Cohen’s kappa of 0.9767, and Matthews correlation coefficient of 0.9766. Further validation through interpretability techniques such as Grad-CAM and Grad-CAM++ illustrated the model's precise identification of clinically significant lesion areas, thereby enhancing confidence and interpretability of predictions. Its compactness, computational efficiency, and superior accuracy demonstrate significant potential for real-time diagnostics, especially beneficial in resource-limited healthcare environments, facilitating early diagnosis and intervention for arsenic-related conditions. Arsenic poisoning Deep learning Skin lesion classification Hybrid CNN Explainable AI 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. 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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-6612143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459303512,"identity":"8145d47b-23be-45c9-b81a-384b49419149","order_by":0,"name":"Tasnim Sultana Sintheia","email":"","orcid":"","institution":"American International University-Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Tasnim","middleName":"Sultana","lastName":"Sintheia","suffix":""},{"id":459303516,"identity":"56f6ecfb-8181-41b0-b5df-3a66b9f67a52","order_by":1,"name":"Md. 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It leads to severe medical conditions such as skin lesions, cancer, and cardiovascular ailments. This study introduces an advanced deep learning framework designed to automatically detect and classify arsenic-induced skin lesions using images captured via smartphones in Bangladesh. The developed hybrid convolutional neural network (CNN) architecture combines parallel CNN pathways, Dense and Residual blocks for efficient feature extraction, and lightweight Fire modules to optimize computational performance. Preprocessing of images involved resizing, normalization, and specialized augmentation strategies to mitigate class imbalances and enhance the training efficacy. The model achieved outstanding accuracy of 98.33%, surpassing the performance of standard CNNs (DenseNet121, ResNet50V2) and other hybrid configurations. Detailed performance assessments confirmed robust predictive power, yielding precision of 0.9900, recall of 0.9867, F1-score of 0.9883, specificity of 0.9867, Cohen\u0026rsquo;s kappa of 0.9767, and Matthews correlation coefficient of 0.9766. Further validation through interpretability techniques such as Grad-CAM and Grad-CAM++ illustrated the model's precise identification of clinically significant lesion areas, thereby enhancing confidence and interpretability of predictions. Its compactness, computational efficiency, and superior accuracy demonstrate significant potential for real-time diagnostics, especially beneficial in resource-limited healthcare environments, facilitating early diagnosis and intervention for arsenic-related conditions.\u003c/p\u003e","manuscriptTitle":"A Lightweight Hybrid CNN Model for Classification of Arsenic-Induced Skin Lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 12:14:50","doi":"10.21203/rs.3.rs-6612143/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"7837eb9d-c9f1-45ac-934b-ca6265625b53","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T08:54:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-21 12:14:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6612143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6612143","identity":"rs-6612143","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}