Hybrid Metaheuristic Contrast Stretching for Enhanced Segmentation of Skin Cancer Lesions

Hybrid Metaheuristic Contrast Stretching for Enhanced Segmentation of Skin Cancer 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 Hybrid Metaheuristic Contrast Stretching for Enhanced Segmentation of Skin Cancer Lesions Kaynat Rana, Muhammad Abrar Ahmad Khan, Tallha Akram, Inzamam Mashood Nasir, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8714892/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 World Health Organization (WHO) estimates that one in three individuals will likely get skin cancer in their lifetime, rendering it one of the most common cancer types globally. A study conducted in the UK indicates that although hereditary and lifestyle factors contribute to merely 14% of melanoma incidences, extended sun exposure is the primary cause. The mortality rate may ascend to 41% if treatment is postponed beyond two to three months, in contrast to merely 5% when identified early, underscoring the vital significance of prompt and precise diagnosis. In medical image analysis, preprocessing is a crucial step that optimizes image quality by enhancing contrast and highlighting lesion boundaries. Improved contrast assists dermatologists in visual examination and markedly enhances the efficacy of automated classification and segmentation algorithms. Current preprocessing techniques frequently lack the ability to produce uniform outcomes across varied datasets due to their restricted adaptability. This paper presents a novel metaheuristic preprocessing strategy, DE-BA-ABC, which amalgamates Differential Evolution (DE), Bat Algorithm (BA), and Artificial Bee Colony (ABC) optimization methodologies to tackle this difficulty. The proposed method integrates the exploration and exploitation capabilities of these algorithms to adaptively estimate optimal contrast values for each image, resulting in consistently improved outputs. The efficacy of the method is confirmed using three publicly accessible skin lesion datasets—PH2, ISIC-2016, and ISIC-2017—utilizing the UNet segmentation framework. Performance is assessed using established criteria, such as the Jaccard Index and Dice Coefficient. Experimental results indicate a significant improvement: the Jaccard Index rises from 84.71% to 93.65%, and the Dice Coefficient climbs from 89.01% to 92.57%. The results validate that the suggested DE-BA-ABC preprocessing technique significantly improves lesion segmentation performance, presenting a viable avenue for the advancement of more dependable computer-aided diagnostic systems for skin cancer diagnosis. Bat Algorithm Artificial Bee Colony Differential Evolution Skin Lesion Machine and Deep Learning 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-8714892","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585928522,"identity":"60c22e88-1527-4267-aad3-6f0a666ec65f","order_by":0,"name":"Kaynat Rana","email":"","orcid":"","institution":"HITEC University Taxila","correspondingAuthor":false,"prefix":"","firstName":"Kaynat","middleName":"","lastName":"Rana","suffix":""},{"id":585928523,"identity":"3a3d2470-6ead-4042-ac00-d87231f8492e","order_by":1,"name":"Muhammad Abrar Ahmad Khan","email":"","orcid":"","institution":"HITEC University Taxila","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Abrar Ahmad","lastName":"Khan","suffix":""},{"id":585928524,"identity":"2b5f239b-325d-4bfa-b67e-4db112d17b6c","order_by":2,"name":"Tallha Akram","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Tallha","middleName":"","lastName":"Akram","suffix":""},{"id":585928525,"identity":"57ef108a-fdec-42a2-a55a-a84983f72e6e","order_by":3,"name":"Inzamam Mashood Nasir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACNmTOAYYKEMXYQIqWM0RoQQWMbUQo4pNuf/jpRs02BnP24w8PV87blm9wu7mB4SMevWwyB5Klc47dZrDsyTE4eHbbbcsNdw42MM7Ep0Ui4YB0DtttBoMDOQwHG7fdNjC4kdjAzItXS2Lz75x/QC3nnz842DgHquUvXi3JbNK5bUAtNxIMDjY2QLXgCwc2iTQ269y+2zwGN94YHGw4dttAEqjlYM853FrkZ6Q/vp3z7bacwfn0xx8bam4b8N1If/jgRxluLTDAg8I7QFjDKBgFo2AUjAJ8AAASnVkaY/4MIAAAAABJRU5ErkJggg==","orcid":"","institution":"Mykolas Romeris University","correspondingAuthor":true,"prefix":"","firstName":"Inzamam","middleName":"Mashood","lastName":"Nasir","suffix":""},{"id":585928526,"identity":"2c3272cb-33d6-4c11-a2c8-4f1667dacbb0","order_by":4,"name":"Sara Tehsin","email":"","orcid":"","institution":"Kaunas University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Tehsin","suffix":""}],"badges":[],"createdAt":"2026-01-28 00:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8714892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8714892/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102183511,"identity":"ddd45caa-e5c9-4b9a-aa90-706757443668","added_by":"auto","created_at":"2026-02-09 07:49:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1115188,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8714892/v1_covered_742ed066-aff3-40fc-9127-b68b5a9d7f14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Metaheuristic Contrast Stretching for Enhanced Segmentation of Skin Cancer Lesions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Bat Algorithm, Artificial Bee Colony, Differential Evolution, Skin Lesion, Machine and Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-8714892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8714892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe World Health Organization (WHO) estimates that one in three individuals will likely get skin cancer in their lifetime, rendering it one of the most common cancer types globally. A study conducted in the UK indicates that although hereditary and lifestyle factors contribute to merely 14% of melanoma incidences, extended sun exposure is the primary cause. The mortality rate may ascend to 41% if treatment is postponed beyond two to three months, in contrast to merely 5% when identified early, underscoring the vital significance of prompt and precise diagnosis. In medical image analysis, preprocessing is a crucial step that optimizes image quality by enhancing contrast and highlighting lesion boundaries. Improved contrast assists dermatologists in visual examination and markedly enhances the efficacy of automated classification and segmentation algorithms. Current preprocessing techniques frequently lack the ability to produce uniform outcomes across varied datasets due to their restricted adaptability. This paper presents a novel metaheuristic preprocessing strategy, DE-BA-ABC, which amalgamates Differential Evolution (DE), Bat Algorithm (BA), and Artificial Bee Colony (ABC) optimization methodologies to tackle this difficulty. The proposed method integrates the exploration and exploitation capabilities of these algorithms to adaptively estimate optimal contrast values for each image, resulting in consistently improved outputs. The efficacy of the method is confirmed using three publicly accessible skin lesion datasets\u0026mdash;PH2, ISIC-2016, and ISIC-2017\u0026mdash;utilizing the UNet segmentation framework. Performance is assessed using established criteria, such as the Jaccard Index and Dice Coefficient. Experimental results indicate a significant improvement: the Jaccard Index rises from 84.71% to 93.65%, and the Dice Coefficient climbs from 89.01% to 92.57%. The results validate that the suggested DE-BA-ABC preprocessing technique significantly improves lesion segmentation performance, presenting a viable avenue for the advancement of more dependable computer-aided diagnostic systems for skin cancer diagnosis.\u003c/p\u003e","manuscriptTitle":"Hybrid Metaheuristic Contrast Stretching for Enhanced Segmentation of Skin Cancer Lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 07:49:23","doi":"10.21203/rs.3.rs-8714892/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":"010cddf6-d37b-4b48-bc10-8867cd486a62","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T20:24:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 07:49:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8714892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8714892","identity":"rs-8714892","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}