OsteoCancerNet: An Efficient and Fast Bone Cancer Diagnostic Model Combining EfficientNet B4 and SVM with RBF Kernel for X-ray Image Analysis

OsteoCancerNet: An Efficient and Fast Bone Cancer Diagnostic Model Combining EfficientNet B4 and SVM with RBF Kernel for X-ray Image Analysis | 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 OsteoCancerNet: An Efficient and Fast Bone Cancer Diagnostic Model Combining EfficientNet B4 and SVM with RBF Kernel for X-ray Image Analysis Nashaat M. Hussain Hassan, Ahmed S. Bayoumy, Mohamed Hassan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6530365/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Bone cancer diagnosis is imperative for diagnosing and treating many forms of primary and metastatic bone cancers early. Traditional imaging techniques, such as CT, MRI, and X-ray scans, are effective but typically involve manual interpretation, which is laborious and prone to human mistake. With increased accuracy, dependability, and efficiency over conventional techniques, automated bone cancer diagnosis systems have been made possible by recent developments in machine learning (ML) and deep learning (DL). Even though a large portion of the recent literature has made significant strides in the diagnosis of bone cancer using deep learning techniques, many of these methods have numerous drawbacks, including computational complexity, overfitting in certain situations, and a lack of reliable databases. The objective of this research is to develop a method that can diagnose bone cancer as quickly, efficiently, and affordably as feasible. This method's main contribution is the combination of EfficientNetB4 and SVM algorithms, which improves accuracy, speed, and accessibility while making use of large datasets and reliable assessment measures. Combining EfficientNetB4 and SVM is crucial for diagnosing bone cancer because it harnesses the EfficientNetB4 technique's amazing capacity to extract useful features both quantitatively and qualitatively, as well as the SVM's significant advantage when it comes to binary separation. After a thorough analysis of numerous research, it was determined to combine these methods as they are distinguished by their great efficiency and simplicity in job implementation, especially in medical environments where accuracy and interpretability are of utmost importance The success of the suggested methods was shown by experiments on a large dataset (which contains 35244 X-ray images), which produced 98% precision, 97.47% recall, 98% accuracy, and 98% F1-score. The suggested method's better performance and computational economy are highlighted by comparison with machine learning, deep learning, and transfer learning techniques. Furthermore, the suggested system encounters a quick inference time of 41 ms, which qualifies it for clinical real-time applications. This study offers a viable strategy for early detection and better treatment outcomes by demonstrating the potential of integrating deep learning and traditional machine learning approaches for better bone cancer diagnosis. Biological sciences/Cancer/Bone cancer Physical sciences/Engineering/Biomedical engineering OsteoCancerNet Computer-assisted diagnosis Bone Cancer Diogenes EfficientNet B4 Model SVM Model X-ray Image Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers invited by journal 12 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Editor invited by journal 12 May, 2025 Submission checks completed at journal 09 May, 2025 First submitted to journal 25 Apr, 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-6530365","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502565695,"identity":"48e86527-b397-46c0-be67-2faa785910cd","order_by":0,"name":"Nashaat M. 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Traditional imaging techniques, such as CT, MRI, and X-ray scans, are effective but typically involve manual interpretation, which is laborious and prone to human mistake. With increased accuracy, dependability, and efficiency over conventional techniques, automated bone cancer diagnosis systems have been made possible by recent developments in machine learning (ML) and deep learning (DL). Even though a large portion of the recent literature has made significant strides in the diagnosis of bone cancer using deep learning techniques, many of these methods have numerous drawbacks, including computational complexity, overfitting in certain situations, and a lack of reliable databases. The objective of this research is to develop a method that can diagnose bone cancer as quickly, efficiently, and affordably as feasible. 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This study offers a viable strategy for early detection and better treatment outcomes by demonstrating the potential of integrating deep learning and traditional machine learning approaches for better bone cancer diagnosis.\u003c/p\u003e","manuscriptTitle":"OsteoCancerNet: An Efficient and Fast Bone Cancer Diagnostic Model Combining EfficientNet B4 and SVM with RBF Kernel for X-ray Image Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 18:44:47","doi":"10.21203/rs.3.rs-6530365/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T06:35:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T15:23:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T05:35:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184686971527573176692776582902843541024","date":"2025-09-11T14:34:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T03:55:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T05:36:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310455348532606442415292186113326555240","date":"2025-08-19T12:33:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78680553560936698869667062340627640770","date":"2025-08-19T06:22:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144999193479279629525085232870596624757","date":"2025-08-12T16:21:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T07:13:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T07:39:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-12T10:33:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-09T14:00:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-25T15:44:43+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":"c8773e16-1ff5-4ad0-9ef8-6d70bf33237d","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53386048,"name":"Biological sciences/Cancer/Bone cancer"},{"id":53386049,"name":"Physical sciences/Engineering/Biomedical engineering"}],"tags":[],"updatedAt":"2026-03-09T16:14:40+00:00","versionOfRecord":{"articleIdentity":"rs-6530365","link":"https://doi.org/10.1038/s41598-026-38801-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-03 15:58:35","publishedOnDateReadable":"March 3rd, 2026"},"versionCreatedAt":"2025-08-20 18:44:47","video":"","vorDoi":"10.1038/s41598-026-38801-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-38801-3","workflowStages":[]},"version":"v1","identity":"rs-6530365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6530365","identity":"rs-6530365","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}