Diagnosing Skin Cancer Using Shearlet Transform Multiresolution Computation

Diagnosing Skin Cancer Using Shearlet Transform Multiresolution Computation | 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 Short Report Diagnosing Skin Cancer Using Shearlet Transform Multiresolution Computation Abdul Razak Mohamed Sikkander, Maheshkumar H. Kolekar, Vidya Lakshmi v, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4772856/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 Skin cancer diagnosis relies on the accurate analysis of medical images to identify malignant and benign lesions. The Shearlet transform, a powerful mathematical tool for multiresolution analysis, has shown promise in enhancing the detection and classification of skin cancer. This study investigates the application of Shearlet transform-based multiresolution analysis in skin cancer diagnosis. The Shearlet transform, known for its ability to capture anisotropic features and directional information, provides a comprehensive representation of skin lesion images at multiple scales and orientations. We integrate the Shearlet transform with advanced image processing techniques to extract discriminative features from dermoscopic images. These features are then utilized to train a machine learning classifier, specifically a support vector machine (SVM), to distinguish between malignant and benign skin lesions. The proposed methodology is evaluated on a publicly available dataset, and the results demonstrate significant improvements in diagnostic accuracy compared to traditional methods. Our approach enhances feature extraction capabilities, leading to more reliable and precise skin cancer diagnosis, ultimately contributing to better patient outcomes. Medical Imaging Directional Information Machine Learning Support Vector Machine (SVM) Image Processing Malignant Lesions Diagnostic Accuracy Patient Outcomes Full Text Additional Declarations No competing interests reported. Supplementary Files T.P29072024.docx T.P29072024.docx 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-4772856","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":344791341,"identity":"407be4af-88cb-4309-ab2b-ad012c639cee","order_by":0,"name":"Abdul Razak Mohamed Sikkander","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHACxgcSBjZy/OwNQLaBBVFamA0sKtKMJXsOgLRIEKWFTaLizOFEgxsJIA4RWvinHX8gcbMtLUFy5vOrG34USDDwt3cn4NUicTvHwHBmm00ev3RO2c0eoMMkzpzdgN+a2zkMyZJtacWSs3PSbvAAtRhI5OLXIn87/cHhv22HEzfcPJN28w8xWgxuJxg2SAC9v+EG+7HbRNlieDvHmEECHMg5bLdlDCR4CPpF7nb68x+QqDz+7OabP0BGey8B7yMAjwGYJFY5CLA/IEX1KBgFo2AUjCAAAFy8TIIU0dG0AAAAAElFTkSuQmCC","orcid":"","institution":"Velammal Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Abdul","middleName":"Razak Mohamed","lastName":"Sikkander","suffix":""},{"id":344791342,"identity":"d87172a6-7cf8-4264-8f6c-e4ad8ce59d3a","order_by":1,"name":"Maheshkumar H. 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