ArticleScientific reports2021
Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 79 citations in OpenAlex.
- Integration of VectorCam, an AI-Enabled Mosquito Surveillance Tool, Into Uganda's National Malaria Elimination Program: Protocol for a Mixed Methods Hybrid Type III Implementation Study.JMIR public health and surveillance · 2026Article
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
- Horizons of the Future: Preparedness and Response.Current topics in microbiology and immunology · 2026Review
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
- Advancing insect monitoring: analysis of mel-frequency cepstral coefficients from optical signals for body orientation estimation.Applied physics. B, Lasers and optics · 2026Article
- Range expansion ofOne health (Amsterdam, Netherlands) · 2025Article
- Human activity-aware coverage path planning for robot-based mosquito control.Scientific reports · 2025Article
- Application of wings interferential patterns (WIPs) and deep learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance.Scientific reports · 2025Article
- Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification.Scientific data · 2025Article
- Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025Review
- TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model.PloS one · 2025Article
- Enhance fashion classification of mosquito vector species via self-supervised vision transformer.Scientific reports · 2024Article
- Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae).Animals : an open access journal from MDPI · 2024Article
- Advanced vision transformers and open-set learning for robust mosquito classification: A novel approach to entomological studies.PLoS computational biology · 2024Article
- Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications.Scientific reports · 2024Article
- Robust mosquito species identification from diverse body and wing images using deep learning.Parasites & vectors · 2024Article
- A Handheld Tool for the Rapid Morphological Identification of Mosquito Species (VectorCam) for Community-Based Malaria Vector Surveillance: Summative Usability Study.JMIR human factors · 2024Article
- AI-driven convolutional neural networks for accurate identification of yellow fever vectors.Parasites & vectors · 2024Article
- Experimental evaluation of genetic variability based on DNA metabarcoding from the aquatic environment: Insights from the LerayEcology and evolution · 2024Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.