Efficient Convolutional Neural Networks for Acute Lymphoblastic Leukaemia Prediction in Computer Vision

Efficient Convolutional Neural Networks for Acute Lymphoblastic Leukaemia Prediction in Computer Vision | 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 Article Efficient Convolutional Neural Networks for Acute Lymphoblastic Leukaemia Prediction in Computer Vision S B MOHAN, Sathya S, Rajalaksmi S, Gurumoorthy, G, Rajkumar Sivanraju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7855315/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract A dangerous hematological malignancy, acute lymphoblastic leukemia (ALL) has a survival rate that is drastically affected by how long it takes to diagnose the disease. Though convolutional neural networks (CNNs) have improved medical imaging, the clinical dependability of most previous research is limited due to their reliance on single models, imbalance in the datasets, and absence of statistical validation. This study proposes an ensemble framework integrating pre-trained CNNs (DenseNet-121, ResNet-34) for feature extraction with machine learning classifiers—Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), AdaBoost, and Backpropagation Network (BPN). Experiments on the C-NMC leukemia dataset (10,661 images) show that the ensemble achieves 92.5% accuracy and 93.1% F1-score, outperforming DenseNet-121 and ResNet-34 by 5.6% and 6.3%, respectively. The model also records the highest AUC (0.975) across classifiers. Statistical tests ( t -test, Wilcoxon) confirm that the improvements are significant ( p < 0.05). The proposed method demonstrates practical potential as an automated clinical decision-support tool, reducing manual interpretation errors and expediting diagnosis. By combining CNN-based deep features with ensemble machine learning, the framework improves robustness, sensitivity, and applicability in real-world hematology workflows. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Convolutional Neural Networks (CNNs) Acute Lymphoblastic Leukemia (ALL) Machine Learning Ensemble Learning Medical Image Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 20 Oct, 2025 Editor invited by journal 20 Oct, 2025 Submission checks completed at journal 19 Oct, 2025 First submitted to journal 19 Oct, 2025 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-7855315","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":537257062,"identity":"75df7e2a-4ba3-4776-ac0a-6e44bf942728","order_by":0,"name":"S B MOHAN","email":"","orcid":"","institution":"S.A.Engineering College","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"B","lastName":"MOHAN","suffix":""},{"id":537257063,"identity":"7be5423f-3737-4f3c-98a4-717507b712ba","order_by":1,"name":"Sathya S","email":"","orcid":"","institution":"S.A.Engineering 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Vision","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Convolutional Neural Networks (CNNs), Acute Lymphoblastic Leukemia (ALL), Machine Learning, Ensemble Learning, Medical Image Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7855315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7855315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA dangerous hematological malignancy, acute lymphoblastic leukemia (ALL) has a survival rate that is drastically affected by how long it takes to diagnose the disease. Though convolutional neural networks (CNNs) have improved medical imaging, the clinical dependability of most previous research is limited due to their reliance on single models, imbalance in the datasets, and absence of statistical validation. This study proposes an ensemble framework integrating pre-trained CNNs (DenseNet-121, ResNet-34) for feature extraction with machine learning classifiers\u0026mdash;Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), AdaBoost, and Backpropagation Network (BPN). Experiments on the C-NMC leukemia dataset (10,661 images) show that the ensemble achieves 92.5% accuracy and 93.1% F1-score, outperforming DenseNet-121 and ResNet-34 by 5.6% and 6.3%, respectively. The model also records the highest AUC (0.975) across classifiers. Statistical tests (\u003cem\u003et\u003c/em\u003e-test, Wilcoxon) confirm that the improvements are significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The proposed method demonstrates practical potential as an automated clinical decision-support tool, reducing manual interpretation errors and expediting diagnosis. By combining CNN-based deep features with ensemble machine learning, the framework improves robustness, sensitivity, and applicability in real-world hematology workflows.\u003c/p\u003e","manuscriptTitle":"Efficient Convolutional Neural Networks for Acute Lymphoblastic Leukaemia Prediction in Computer Vision","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 12:56:15","doi":"10.21203/rs.3.rs-7855315/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-06T06:14:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T16:31:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87717861629067282534412367576463701047","date":"2025-11-04T16:25:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T05:23:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17370482968162029086193126271910998954","date":"2025-10-29T12:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325898683668359520553566051777902140033","date":"2025-10-21T05:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T01:08:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T01:03:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T11:19:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-19T13:47:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-19T13:44:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1cdff271-4188-486f-b005-050a7862570f","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57134206,"name":"Biological sciences/Cancer"},{"id":57134207,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":57134208,"name":"Health sciences/Health care"},{"id":57134209,"name":"Physical sciences/Mathematics and computing"},{"id":57134210,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-12-22T16:02:51+00:00","versionOfRecord":{"articleIdentity":"rs-7855315","link":"https://doi.org/10.1038/s41598-025-30777-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-16 15:58:17","publishedOnDateReadable":"December 16th, 2025"},"versionCreatedAt":"2025-10-31 12:56:15","video":"","vorDoi":"10.1038/s41598-025-30777-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30777-w","workflowStages":[]},"version":"v1","identity":"rs-7855315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7855315","identity":"rs-7855315","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}