Application of Wings Interferential Patterns (WIPs) and Deep Learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance

Application of Wings Interferential Patterns (WIPs) and Deep Learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance | 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 Application of Wings Interferential Patterns (WIPs) and Deep Learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance Arnaud Cannet, Camille Simon-Chane, Aymeric Histace, Mohammad Akhoundi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5543317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract In this paper, we test the possibility of using Wing Interference Patterns (WIPs) and deep learning (DL) for the identification of Culex mosquitoes species to evaluate the extent to which a generic method could be developed for surveying Dipteran insects of major importance to human health. Previous applications of WIPs and DL have successfully demonstrated their utility in identifying Anopheles, Aedes, sandflies, and tsetse flies, providing the rationale for extending this approach to Culex. Accurate identification of these mosquitoes is crucial for vector-borne disease control, yet traditional methods remain labor-intensive and are often hindered by cryptic species or damaged samples. To address these challenges, we applied WIPs, generated by thin-film interference on wing membranes, in combination with convolutional neural networks (CNNs) for species classification. Our results achieved over 99% genus-level accuracy and up to 100% species-level accuracy. Nonetheless, challenges with underrepresented species emphasize the need for larger datasets and complementary techniques such as molecular barcoding. This study highlights the potential of WIPs and DL to enhance mosquito identification and contribute to scalable tools for broader surveys of health-relevant Dipteran insects. Earth and environmental sciences/Ecology/Invasive species Biological sciences/Evolution/Taxonomy Biological sciences/Zoology/Entomology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviews received at journal 07 Mar, 2025 Reviewers agreed at journal 26 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviewers invited by journal 19 Dec, 2024 Editor assigned by journal 19 Dec, 2024 Editor invited by journal 09 Dec, 2024 Submission checks completed at journal 08 Dec, 2024 First submitted to journal 28 Nov, 2024 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. 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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-5543317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":395862387,"identity":"fbba479f-fe92-4320-8cc8-c518bf2e17d3","order_by":0,"name":"Arnaud Cannet","email":"","orcid":"","institution":"Agence Régionale de Santé Ile-de-France","correspondingAuthor":false,"prefix":"","firstName":"Arnaud","middleName":"","lastName":"Cannet","suffix":""},{"id":395862388,"identity":"2ad04715-c5d9-4038-af46-ae6487f9a429","order_by":1,"name":"Camille Simon-Chane","email":"","orcid":"","institution":"École Nationale Supérieure de l'Électronique et de ses 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