A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture

A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture | 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 A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture Marzieh Zare Nazari, Mohsen Sardari Zarchi, Sima Emadi, Hadi Poormohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7711858/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Accurate mosquito species recognition underpins vector surveillance and targeted control, yet field imagery suffers from device variability, clutter, and fine-grained inter-species similarity. Deep learning has emerged as a scalable path, but prior systems often lack calibrated probabilities and degrade under domain shift. We propose a dual-head architecture that aligns an 8-class head with an auxiliary 8 to 2 Aedes head to sharpen difficult boundaries, and we fuse heterogeneous CNN/Transformer branches via calibrated logit stacking followed by temperature scaling (specifically, a CNN backbone paired with a Swin-T Transformer branch to capture complementary local texture and long-range morphology). With test-time augmentation (TTA, 5–8 views), the pipeline jointly reduces variance, corrects bias, and improves posterior calibration. We evaluate on AMID v1 (8-class, whole-body images) and on an unseen, phone-style Aedes corpus used strictly as test-only to probe cross-dataset generalization. Against strong baselines (ResNet-50, EfficientNet-V2-S) and naïve probability averaging, our method attains near-ceiling in-domain performance—Macro-F1 ≈ 99.3–99.4% and Micro-Accuracy ≈ 99.4–99.5%—and exceeds 99% accuracy on the unseen Aedes set, while markedly improving calibration (ECE ≈ 0.6%). Confidence intervals (Wilson, 95%) and paired tests (McNemar) indicate that these gains, though incremental, are consistent and statistically reliable. Ablations show that TTA=5 with calibrated stacking captures most benefits at practical latency. By coupling boundary-aware supervision with calibration-aware fusion, the proposed approach delivers predictions that are both more correct and more trustworthy, stabilizing operating thresholds across sites and capture pipelines —with the Swin-T branch contributing robustness to pose and device variation through its windowed self-attention. This provides a deployment-ready baseline for public-health monitoring and a principled foundation for future extensions to open-set recognition, domain-aware calibration, and multimodal sensing. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Mosquito species classification Dual-head architecture Calibration-aware fusion Test-time augmentation (TTA) Cross-dataset generalization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 06 Oct, 2025 Editor invited by journal 01 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 30 Sep, 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. 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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-7711858","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":543469705,"identity":"c78869dd-dbb6-4e83-bdcd-2a0a7bbefaab","order_by":0,"name":"Marzieh Zare Nazari","email":"","orcid":"","institution":"Islamic Azad University, Yazd","correspondingAuthor":false,"prefix":"","firstName":"Marzieh","middleName":"Zare","lastName":"Nazari","suffix":""},{"id":543469706,"identity":"f7a54db6-c50f-4f11-baf7-391f13faef64","order_by":1,"name":"Mohsen Sardari 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Deep learning has emerged as a scalable path, but prior systems often lack calibrated probabilities and degrade under domain shift. We propose a dual-head architecture that aligns an 8-class head with an auxiliary 8 to 2 Aedes head to sharpen difficult boundaries, and we fuse heterogeneous CNN/Transformer branches via calibrated logit stacking followed by temperature scaling (specifically, a CNN backbone paired with a Swin-T Transformer branch to capture complementary local texture and long-range morphology). With test-time augmentation (TTA, 5–8 views), the pipeline jointly reduces variance, corrects bias, and improves posterior calibration. We evaluate on AMID v1 (8-class, whole-body images) and on an unseen, phone-style Aedes corpus used strictly as test-only to probe cross-dataset generalization. Against strong baselines (ResNet-50, EfficientNet-V2-S) and naïve probability averaging, our method attains near-ceiling in-domain performance—Macro-F1 ≈ 99.3–99.4% and Micro-Accuracy ≈ 99.4–99.5%—and exceeds 99% accuracy on the unseen Aedes set, while markedly improving calibration (ECE ≈ 0.6%). Confidence intervals (Wilson, 95%) and paired tests (McNemar) indicate that these gains, though incremental, are consistent and statistically reliable. Ablations show that TTA=5 with calibrated stacking captures most benefits at practical latency. By coupling boundary-aware supervision with calibration-aware fusion, the proposed approach delivers predictions that are both more correct and more trustworthy, stabilizing operating thresholds across sites and capture pipelines —with the Swin-T branch contributing robustness to pose and device variation through its windowed self-attention. 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