{"paper_id":"2f11d0d3-780e-4919-be03-a212fa25bffe","body_text":"ViT-FLSFO: A method for Cervical Image Classification | 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 ViT-FLSFO: A method for Cervical Image Classification Qingbin Fang, Renling Zou, Jing Xu, Rui Guan, Xuelian Gu, Xiufang Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4900930/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 Cervical cancer ranks as the fourth most prevalent cancer among women worldwide. Early diagnosis facilitates timely intervention and treatment. Traditional colposcopy is a widely employed technique for evaluating cervical lesions. Recently, deep learning has been increasingly applied to the diagnosis of cervical diseases. However, conventional Vision Transformer (ViT) models face limitations in effectively extracting localized information, posing challenges in image recognition. This study introduces a novel model for classifying cervical images into normal, cervical intraepithelial neoplasia, and cancerous categories. The model incorporates a Feature-token Labeling Selection Module, Feature Fusion Module，and Overlapping Sampling Module to improve the ViT model (ViT-FLSFO). This module can realize the extraction of local feature areas and short-range interaction in the image, and enhance the edge capturing ability of the model and the proficiency of solving complex problems, thereby elevating prediction accuracy. Empirical results demonstrate that the ViT-FLSFO model achieves a detection accuracy of 91.88% in the cervical image dataset, a precision of 92.91%, a recall of 91.92% and an F1-Score of 91.99%, surpassing other advanced models. Consequently, this model holds significant potential for rapid auxiliary diagnosis in cervical imaging, contributing to the early detection and treatment of cervical cancer. ViT-FLSFO Model Cervical Image Recognition Feature-token Labeling Selection Vision Transformer 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. 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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-4900930\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":339263717,\"identity\":\"4c9c6ff2-be62-4c82-9ee7-16e64a6731a3\",\"order_by\":0,\"name\":\"Qingbin Fang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Shanghai for Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingbin\",\"middleName\":\"\",\"lastName\":\"Fang\",\"suffix\":\"\"},{\"id\":339263718,\"identity\":\"8be78e42-2216-40e2-892e-d5f97c7c13a4\",\"order_by\":1,\"name\":\"Renling 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Early diagnosis facilitates timely intervention and treatment. Traditional colposcopy is a widely employed technique for evaluating cervical lesions. Recently, deep learning has been increasingly applied to the diagnosis of cervical diseases. However, conventional Vision Transformer (ViT) models face limitations in effectively extracting localized information, posing challenges in image recognition. This study introduces a novel model for classifying cervical images into normal, cervical intraepithelial neoplasia, and cancerous categories. The model incorporates a Feature-token Labeling Selection Module, Feature Fusion Module，and Overlapping Sampling Module to improve the ViT model (ViT-FLSFO). This module can realize the extraction of local feature areas and short-range interaction in the image, and enhance the edge capturing ability of the model and the proficiency of solving complex problems, thereby elevating prediction accuracy. 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