{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:40:57Z","timestamp":1775673657509,"version":"3.50.1"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Mosquito-borne diseases represent a significant global health challenge. Over 700,000 people succumb to mosquito-borne diseases annually, highlighting the important need for accurate and efficient mosquito classification systems. Current approaches face limitations in accuracy, computational efficiency, and interpretability, creating a gap that artificial intelligence can help address. This paper presents MosQNet-SA, a novel convolutional-attention network designed for mosquito classification that addresses these limitations through architectural choices. The proposed model incorporates a spatial attention mechanism and depthwise separable convolutions to enhance feature extraction while maintaining computational efficiency\u2014achieving comparable performance with 10-fold fewer parameters than existing approaches. MosQNet-SA achieves 99.42% accuracy on a dataset of 1,000 images across three mosquito species (\n                    <jats:italic>Aedes<\/jats:italic>\n                    ,\n                    <jats:italic>Anopheles<\/jats:italic>\n                    , and\n                    <jats:italic>Culex<\/jats:italic>\n                    ), demonstrating strong performance compared to existing CNN architectures. The model\u2019s explainability is enhanced through multiple methods, including Saliency, GradCAM, LIME, and Kernel SHAP, providing valuable insights into the decision-making process for public health practitioners. Additionally, we present a RESTful API implementation for real-time mosquito classification and disease risk mapping, demonstrating the practical applicability of our approach in public health surveillance systems.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0344970","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:27:17Z","timestamp":1775669237000},"page":"e0344970","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["MosQNet-SA: Explainable convolutional-attention network for mosquito classification with application as a RESTful API for dengue and malaria risk mapping"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1894-9533","authenticated-orcid":true,"given":"Md. Akmol","family":"Masud","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjida","family":"Akter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3054-9432","authenticated-orcid":true,"given":"Nadia","family":"Sultana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Shahidul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8065-7173","authenticated-orcid":true,"given":"Mohammed","family":"Abu Yousuf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2256-3835","authenticated-orcid":true,"given":"Farzan M.","family":"Noori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-1834","authenticated-orcid":true,"given":"Md Zia","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"pone.0344970.ref001","doi-asserted-by":"crossref","unstructured":"Yin MS, Haddawy P, Nirandmongkol B, Kongthaworn T, Chaisumritchoke C, Supratak A, et al. A Lightweight Deep Learning Approach to Mosquito Classification from Wingbeat Sounds. In: Proceedings of the Conference on Information Technology for Social Good, 2021. 37\u201342. https:\/\/doi.org\/10.1145\/3462203.3475908","DOI":"10.1145\/3462203.3475908"},{"key":"pone.0344970.ref002","volume-title":"Vector-borne Diseases","author":"World Health Organization","year":"2024"},{"issue":"3","key":"pone.0344970.ref003","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3390\/insects14030221","article-title":"Mosquito-Borne Diseases and Their Control Strategies: An Overview Focused on Green Synthesized Plant-Based Metallic Nanoparticles","volume":"14","author":"H Onen","year":"2023","journal-title":"Insects"},{"key":"pone.0344970.ref004","doi-asserted-by":"crossref","DOI":"10.3389\/fitd.2023.1240420","article-title":"Control of emerging and re-emerging zoonotic and vector-borne diseases in countries of the Eastern Mediterranean Region","volume":"4","author":"AA Chughtai","year":"2023","journal-title":"Front Trop Dis"},{"key":"pone.0344970.ref005","volume-title":"World Malaria Report 2023","author":"World Health Organization","year":"2023"},{"issue":"4","key":"pone.0344970.ref006","doi-asserted-by":"crossref","DOI":"10.1002\/hsr2.1210","article-title":"Recent outbreak of dengue in Bangladesh: A threat to public health","volume":"6","author":"KK Bhowmik","year":"2023","journal-title":"Health Sci Rep"},{"key":"pone.0344970.ref007","unstructured":"World Health Organization. World Malaria Report 2023. 2023. https:\/\/www.who.int\/teams\/global-malaria-programme\/reports\/world-malaria-report-2023"},{"key":"pone.0344970.ref008","unstructured":"World Health Organization. Disease Outbreak News: 2024-DON518. 2024. https:\/\/www.who.int\/emergencies\/disease-outbreak-news\/item\/2024-DON518"},{"key":"pone.0344970.ref009","unstructured":"World Health Organization. Zika Virus. 2024. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/zika-virus"},{"key":"pone.0344970.ref010","unstructured":"World Health Organization. Lymphatic filariasis. World Health Organization. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/lymphatic-filariasis"},{"key":"pone.0344970.ref011","doi-asserted-by":"crossref","first-page":"107348","DOI":"10.1016\/j.actatropica.2024.107348","article-title":"Smart technology for mosquito control: Recent developments, challenges, and future prospects","volume":"258","author":"P Rajak","year":"2024","journal-title":"Acta Trop"},{"issue":"1","key":"pone.0344970.ref012","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1038\/s41598-020-57875-1","article-title":"Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks","volume":"10","author":"J Park","year":"2020","journal-title":"Sci Rep"},{"issue":"2","key":"pone.0344970.ref013","doi-asserted-by":"crossref","first-page":"113","DOI":"10.52940\/ijici.v2i2.45","article-title":"Mosquito Detection and Classification Using Machine Learning Algorithms","volume":"2","author":"A Alubedy","year":"2023","journal-title":"IJICI"},{"key":"pone.0344970.ref014","article-title":"Deep Learning-based Vector Mosquitoes Classification for Preventing Infectious Diseases Transmission","author":"M Asgari","year":"2022","journal-title":"Image Anal Stereol"},{"key":"pone.0344970.ref015","doi-asserted-by":"crossref","unstructured":"Siddiqui AA, Kayte DrC. Convolution Neural Network-based Mosquito Classification System. In: Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India, 2023. https:\/\/doi.org\/10.4108\/eai.24-3-2022.2318954","DOI":"10.4108\/eai.24-3-2022.2318954"},{"issue":"18","key":"pone.0344970.ref016","doi-asserted-by":"crossref","first-page":"3935","DOI":"10.3390\/app9183935","article-title":"Vision-Based Classification of Mosquito Species: Comparison of Conventional and Deep Learning Methods","volume":"9","author":"K Okayasu","year":"2019","journal-title":"Applied Sciences"},{"issue":"1","key":"pone.0344970.ref017","doi-asserted-by":"crossref","first-page":"3094","DOI":"10.1038\/s41598-024-53631-x","article-title":"A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images","volume":"14","author":"FG Sauer","year":"2024","journal-title":"Sci Rep"},{"issue":"2","key":"pone.0344970.ref018","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/JSTSP.2021.3122886","article-title":"On the Use of Uncertainty in Classifying Aedes Albopictus Mosquitoes","volume":"16","author":"G Adhane","year":"2022","journal-title":"IEEE J Sel Top Signal Process"},{"key":"pone.0344970.ref019","first-page":"193","article-title":"Mosquito on Human Skin Classification Using Deep Learning","volume-title":"Studies in Big Data","author":"CSA Kumar","year":"2023"},{"key":"pone.0344970.ref020","doi-asserted-by":"crossref","first-page":"107089","DOI":"10.1016\/j.actatropica.2023.107089","article-title":"Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification","volume":"249","author":"VR de Lima","year":"2024","journal-title":"Acta Trop"},{"key":"pone.0344970.ref021","doi-asserted-by":"crossref","first-page":"101090","DOI":"10.1016\/j.ecoinf.2020.101090","article-title":"A comparison of supervised machine learning algorithms for mosquito identification from backscattered optical signals","volume":"58","author":"AP Genoud","year":"2020","journal-title":"Ecological Informatics"},{"issue":"1","key":"pone.0344970.ref022","doi-asserted-by":"crossref","first-page":"10334","DOI":"10.1038\/s41598-022-14372-x","article-title":"A ResNet attention model for classifying mosquitoes from wing-beating sounds","volume":"12","author":"X Wei","year":"2022","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0344970.ref023","doi-asserted-by":"crossref","first-page":"22130","DOI":"10.1038\/s41598-023-47266-7","article-title":"Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques","volume":"13","author":"FB Azam","year":"2023","journal-title":"Sci Rep"},{"key":"pone.0344970.ref024","doi-asserted-by":"crossref","unstructured":"Goni MOF, Mondal MNI. Explainable AI Based Malaria Detection Using Lightweight CNN. In: 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), 2023. 1\u20135.","DOI":"10.1109\/NCIM59001.2023.10212621"},{"issue":"7","key":"pone.0344970.ref025","doi-asserted-by":"crossref","first-page":"36","DOI":"10.35940\/ijitee.G9992.0611722","article-title":"A Novel Approach to Explainable AI using Formal Concept Lattice","volume":"11","author":"B Venkatsubramaniam","year":"2022","journal-title":"IJITEE"},{"key":"pone.0344970.ref026","doi-asserted-by":"crossref","first-page":"101241","DOI":"10.1016\/j.ecoinf.2021.101241","article-title":"Review of machine learning techniques for mosquito control in urban environments","volume":"61","author":"A Joshi","year":"2021","journal-title":"Ecological Informatics"},{"key":"pone.0344970.ref027","unstructured":"Rodriguez A, Bartumeus F, Gavald\u00e0 R. Machine Learning Assists the Classification of Reports by Citizens on Disease-Carrying Mosquitoes. In: Proceedings of the ECML-PKDD Workshop on Data Science for Social Good, 2016. https:\/\/api.semanticscholar.org\/CorpusID:6870598"},{"key":"pone.0344970.ref028","unstructured":"Mosquito Alert Consortium. Mosquito Alert Platform. 2024. https:\/\/mosquitoalert.com"},{"key":"pone.0344970.ref029","unstructured":"Pise AN. Mosquito Species Dataset. 2021."},{"key":"pone.0344970.ref030","unstructured":"Pise AN. IEEE DataPort Mosquito Dataset. 2020."},{"key":"pone.0344970.ref031","unstructured":"Dryad Digital Repository. Anopheles mosquito dataset. 2024."},{"key":"pone.0344970.ref032","unstructured":"Masud MA, Akter S, Sultana N, Islam MS, Yousuf MA, Noori FM. MosQNet-SA Dataset: Merged Mosquito Species Classification Dataset. 2024. https:\/\/zenodo.org\/record\/14238701"},{"key":"pone.0344970.ref033","doi-asserted-by":"crossref","unstructured":"Akter M, Hossain MS, Ahmed TU, Andersson K. Mosquito classification using convolutional neural network with data augmentation. International Conference on Intelligent Computing & Optimization. Springer. 2020. 865\u2013879.","DOI":"10.1007\/978-3-030-68154-8_74"},{"issue":"1","key":"pone.0344970.ref034","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.sjbs.2021.09.021","article-title":"Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology","volume":"29","author":"F Rustam","year":"2022","journal-title":"Saudi J Biol Sci"},{"key":"pone.0344970.ref035","doi-asserted-by":"crossref","first-page":"72681","DOI":"10.1109\/ACCESS.2021.3079700","article-title":"A deep convolutional neural network for classification of aedes albopictus mosquitoes","volume":"9","author":"G Adhane","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0344970.ref036","doi-asserted-by":"crossref","unstructured":"Shamim MdAR, Anas ABM, Erfan Md. Identification of Vector and Non-vector Mosquito Species Using Deep Convolutional Neural Networks with Ensemble Model. In: 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), 2022. 1\u20136. https:\/\/doi.org\/10.1109\/icaeee54957.2022.9836382","DOI":"10.1109\/ICAEEE54957.2022.9836382"},{"issue":"1","key":"pone.0344970.ref037","first-page":"18664","article-title":"A Swin Transformer-based model for mosquito species identification","volume":"12","author":"D-z Zhao","year":"2022","journal-title":"Scientific Reports. Nature Publishing Group UK London"},{"key":"pone.0344970.ref038","doi-asserted-by":"crossref","unstructured":"Kumar VS, Prasad VV, Kunaisa K, Swain MP, Anandaram H, Kumar A. Mosquito type identification using convolution neural network. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). IEEE. 2022. 1059\u20131064.","DOI":"10.1109\/ICOSEC54921.2022.9951979"},{"issue":"9","key":"pone.0344970.ref039","doi-asserted-by":"crossref","first-page":"183","DOI":"10.12911\/22998993\/168501","article-title":"A Deep Transfer Learning Framework for the Multi-Class Classification of Vector Mosquito Species","volume":"24","author":"R Pise","year":"2023","journal-title":"J Ecol Eng"},{"issue":"1","key":"pone.0344970.ref040","first-page":"10609","article-title":"Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system","volume":"13","author":"V Kittichai","year":"2023","journal-title":"Scientific Reports. Nature Publishing Group UK London"},{"issue":"6","key":"pone.0344970.ref041","first-page":"1515","article-title":"Classifying the unknown: Insect identification with deep hierarchical Bayesian learning. Methods in Ecology and Evolution","volume":"14","author":"S Badirli","year":"2023","journal-title":"Wiley Online Library"},{"issue":"2","key":"pone.0344970.ref042","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1017\/S000748532400018X","article-title":"Application of computer vision and deep learning models to automatically classify medically important mosquitoes in North Borneo, Malaysia","volume":"114","author":"S-Q Ong","year":"2024","journal-title":"Bulletin of Entomological Research. Cambridge University Press"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0344970","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:27:25Z","timestamp":1775669245000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0344970"}},"subtitle":[],"editor":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4,8]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0344970","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,8]]}}}