A Hybrid Segmentation and Deep Learning Framework for Automated Leukemia Detection in Blood Smear Images

A Hybrid Segmentation and Deep Learning Framework for Automated Leukemia Detection in Blood Smear Images | 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 Segmentation and Deep Learning Framework for Automated Leukemia Detection in Blood Smear Images Abdellatif BOUZID-DAHO, Hassan S. Moussa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9390229/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 Leukemia is a life-threatening hematological malignancy in which rapid and accu rate diagnosis is essential for effective clinical intervention. Conventional manual examination of peripheral blood smear images is labor-intensive, time-consuming, and highly dependent on the expertise of hematologists, leading to potential vari ability in diagnosis. To address these limitations, this study proposes a hybrid automated framework for leukemia detection that integrates advanced image segmentation and deep learning-based classification. The proposed approach consists of a dual-stage pipeline. In the first stage, a hybrid segmentation method combining K-means clustering and region growing is employed to accurately isolate white blood cells and extract cellular regions of interest from blood smear images. This hybrid technique leverages the strengths of both unsupervised clustering and spatial connectivity to improve segmentation precision. In the second stage, the segmented cell regions are classified using a pre-trained MobileNetV2 convolutional neural network. Experimental results demonstrate that the proposed framework achieves high segmentation quality and classification performance, confirming its effective ness and robustness. The model shows strong accuracy in detecting leukemia, highlighting its potential as a reliable computer-aided diagnostic tool. Artificial Intelligence and Machine Learning Biomedical Engineering Leukemia deep learning segmentation MobileNetV2 computer-aided diagnostic Full Text Additional Declarations The authors declare no competing interests. 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-9390229","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621516573,"identity":"dac927a0-1bbc-4ba9-829d-6675fef4659a","order_by":0,"name":"Abdellatif BOUZID-DAHO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABMElEQVRIie2RMUvDQBTHXzh4Ltd2vVK1X+GFQB2U+lWuFJw6FAohIMRC4fwGUvEzOASh80mhLiVdCxGMe4eKi+DiRRLBpIqjw/2md+/48X/vDsBi+Y8IIEiHAMg0ZmcOGogBnOT3uFuRBAJRflPOTM1+VyBXIFfmPyqNm0mUSuqGdQRMX1S4X9cseh0Gq97t1eo+BT+shDwuRiSpbwaDPfdaIW9qHLWmy6Q3S/qMIK7GrAcdIYkJbI+xVVOc09OGTPGpoHAULxvt9eDoTdJFlpIpgpPm3ntNxYUiygqZFLP+vFAoUzqm0IVCZcU1u5jBHpoKnUt3GstsF/+YL/ueUTySsSwrh8kk2m6D80ZbsUW69cNT82J3CQ+6B7Ok95x1qh/zhTPe0axkWCwWi+UvfADnD2Y9FMsFcQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0006-6531-8935","institution":"Mouloud Mammeri University, Tizi-Ouzou, Algeria","correspondingAuthor":true,"prefix":"","firstName":"Abdellatif","middleName":"","lastName":"BOUZID-DAHO","suffix":""},{"id":621518571,"identity":"119ac455-0e3f-4893-b0eb-7c6f0ff011b0","order_by":1,"name":"Hassan S. 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Conventional manual examination of peripheral blood smear images is labor-intensive, time-consuming, and highly dependent on the expertise of hematologists, leading to potential vari ability in diagnosis. To address these limitations, this study proposes a hybrid automated framework for leukemia detection that integrates advanced image segmentation and deep learning-based classification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proposed approach consists of a dual-stage pipeline. In the first stage, a hybrid segmentation method combining K-means clustering and region growing is employed to accurately isolate white blood cells and extract cellular regions of interest from blood smear images. This hybrid technique leverages the strengths of both unsupervised clustering and spatial connectivity to improve segmentation precision. 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