A Hybrid Approach for Classification of Lyme Disease using Deep Convolution Neural Networks and Bandelet Transform

A Hybrid Approach for Classification of Lyme Disease using Deep Convolution Neural Networks and Bandelet Transform | 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 Hybrid Approach for Classification of Lyme Disease using Deep Convolution Neural Networks and Bandelet Transform Goriparthi Prathibha, Kotra Sankar Raja Sekhar, Vijay Kotra, Long Chiau Ming, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5197417/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 The bacterium Borrelia burgdorferi is the source of the complicated tick-borne sickness known as Lyme disease. It is essential to accurately categorize Lyme disease into its many stages and symptoms to assist with treatment choices and enhance patient outcomes. In the proposed work, a unique method for categorizing Lyme illness into 15 different classifications by combining the features of Bandelet transform with Convolutional Neural Networks (CNN) which are employed to learn and categorize the discriminative features that were extracted from medical images. Therefore, multiresolution features are extracted using Bandelet Transform. Statistical Texture features such as Mean, Variance, Standard Deviation, Kurtosis and Skewness are calculated from the coefficients of Bandelet Transform which are concatenated with the features extracted from the different pre-trained networks. For the balanced dataset, using EfficientNet-B0 combined with Bandelet transform achieved an IBA of 0.974 and test accuracy of 0.9868. For the unbalanced dataset, using EfficientNetV2B1 with Bandelet transform resulted in an IBA of 0.932 and test accuracy of 0.9812. When classifying the images into 15 different classes, EfficientNetB7 with Bandelet transform achieved an IBA of 0.6424 and test accuracy of 0.7925. The results demonstrate that concatenating pretrained network features with Bandelet transform features improves classification performance for binary classification tasks (balanced and unbalanced datasets), though performance is more limited when extending to 15-class classification. Pre-trained Networks Bandelet Transform Index Balance Accuracy Test Accuracy Human and health communicable disease infectious disease health risk 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-5197417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445905770,"identity":"4d8eafca-d3bf-4df5-a16e-0e0e238f9566","order_by":0,"name":"Goriparthi Prathibha","email":"","orcid":"","institution":"Acharya Nagarjuna University","correspondingAuthor":false,"prefix":"","firstName":"Goriparthi","middleName":"","lastName":"Prathibha","suffix":""},{"id":445905771,"identity":"9db9b1f6-9604-4a46-aebd-19b8560ae967","order_by":1,"name":"Kotra Sankar Raja Sekhar","email":"","orcid":"","institution":"Acharya Nagarjuna 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It is essential to accurately categorize Lyme disease into its many stages and symptoms to assist with treatment choices and enhance patient outcomes. In the proposed work, a unique method for categorizing Lyme illness into 15 different classifications by combining the features of Bandelet transform with Convolutional Neural Networks (CNN) which are employed to learn and categorize the discriminative features that were extracted from medical images. Therefore, multiresolution features are extracted using Bandelet Transform. Statistical Texture features such as Mean, Variance, Standard Deviation, Kurtosis and Skewness are calculated from the coefficients of Bandelet Transform which are concatenated with the features extracted from the different pre-trained networks. For the balanced dataset, using EfficientNet-B0 combined with Bandelet transform achieved an IBA of 0.974 and test accuracy of 0.9868. For the unbalanced dataset, using EfficientNetV2B1 with Bandelet transform resulted in an IBA of 0.932 and test accuracy of 0.9812. When classifying the images into 15 different classes, EfficientNetB7 with Bandelet transform achieved an IBA of 0.6424 and test accuracy of 0.7925. 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