Fetal-Net: Enhancing Maternal-Fetal Ultrasound Interpretation through Multi-Scale Convolutional Neural Networks and Transformers

Fetal-Net: Enhancing Maternal-Fetal Ultrasound Interpretation through Multi-Scale Convolutional Neural Networks and Transformers | 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 Fetal-Net: Enhancing Maternal-Fetal Ultrasound Interpretation through Multi-Scale Convolutional Neural Networks and Transformers Umar Islam, Yasser A. Ali, Muna Al-Razgan, Hanif Ullah, Mohmmed Amin Almaiah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6184392/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of the fetus and image quality fluctuation, its interpretation is quite challenging. Although deep learning include Convolution Neural Networks (CNNs) have been promising, they have largely been limited to one task or the other, such as the segmentation or detection of fetal structures, thus lacking an integrated solution that accounts for the intricate interplay between anatomical structures. To overcome these limitations, Fetal-Net-a new deep learning architecture that integrates Multi-Scale-CNNs and transformer layers-was developed. The model was trained on a large, expertly annotated set of more than 12,000 ultrasound images across different anatomical planes for effective identification of fetal structures and anomaly detection. Fetal-Net, with excellent precision (96.5%), accuracy (97.5%), and recall (97.8%) showed robustness factor against various imaging settings, making it a potent means of augmenting prenatal care through refined ultrasound image interpretation. Biological sciences/Computational biology and bioinformatics/Machine learning Health sciences/Health care/Medical imaging/Radiography Biological sciences/Computational biology and bioinformatics/Image processing Fetus CNN DCNN RF Medical Imaging Transformers Prenatal Care Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Editor invited by journal 18 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 08 Mar, 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. 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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-6184392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430948519,"identity":"3840a46f-c91b-43a3-803d-82b79727c34b","order_by":0,"name":"Umar Islam","email":"","orcid":"","institution":"IQRA National University","correspondingAuthor":false,"prefix":"","firstName":"Umar","middleName":"","lastName":"Islam","suffix":""},{"id":430948520,"identity":"cc6b7ce0-6787-4736-8a2f-b2fc7e1b5865","order_by":1,"name":"Yasser A. 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