ArticleScientific reports2020
Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed.
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- A two-stage OOD-aware approach for mosquito species detection based on RT-DETRv2.Scientific reports · 2026Article
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
- How technologies are driving digital innovation in mosquito surveillance systems: a global scoping review over the past decade.BMJ public health · 2026Article
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- Double-stage insect recognition model based on synergistic localization of key parts.Frontiers in plant science · 2026Article
- Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN).Integrative organismal biology (Oxford, England) · 2026Article
- Range expansion ofOne health (Amsterdam, Netherlands) · 2025Article
- Barcoding Quantitative PCR Assay to Distinguish BetweenTropical medicine and infectious disease · 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
- Enhancing mosquito classification through self-supervised learning.Scientific reports · 2024Article
- Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications.Scientific reports · 2024Article
- AI-driven convolutional neural networks for accurate identification of yellow fever vectors.Parasites & vectors · 2024Article
- Fine-grained image classification on bats using VGG16-CBAM: a practical example with 7 horseshoe bats taxa (CHIROPTERA: Rhinolophidae: Rhinolophus) from Southern China.Frontiers in zoology · 2024Article
- Automated differentiation of mixed populations of free-flying female mosquitoes under semi-field conditions.Scientific reports · 2024Article
- A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images.Scientific reports · 2024Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Image-based automatic classification of vector mosquitoes has been investigated for decades for its practical applications such as early detection of potential mosquitoes-borne diseases. However, the classification accuracy of previous approaches has never been close to human experts' and often images of mosquitoes with certain postures and body parts, such as flatbed wings, are required to achieve good classification performance. Deep convolutional neural networks (DCNNs) are state-of-the-art approach to extracting visual features and classifying objects, and, hence, there exists great interest in applying DCNNs for the classification of vector mosquitoes from easy-to-acquire images. In this study, we investigated the capability of state-of-the-art deep learning models in classifying mosquito species having high inter-species similarity and intra-species variations. Since no off-the-shelf dataset was available capturing the variability of typical field-captured mosquitoes, we constructed a dataset with about 3,600 images of 8 mosquito species with various postures and deformation conditions. To further address data scarcity problems, we investigated the feasibility of transferring general features learned from generic dataset to the mosquito classification. Our result demonstrated that more than 97% classification accuracy can be achieved by fine-tuning general features if proper data augmentation techniques are applied together. Further, we analyzed how this high classification accuracy can be achieved by visualizing discriminative regions used by deep learning models. Our results showed that deep learning models exploit morphological features similar to those used by human experts.
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