ArticleScientific reports2023
Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- 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
- Deadliest Animals with the Thinnest Wings: Near-Infrared Properties of Tropical Mosquitoes.Applied spectroscopy · 2025Article
- Requirements for designing cluster randomised control trials to detect suppression of malaria vector population densities.BMC biology · 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
- An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.Scientific data · 2024Article
- Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP).Scientific reports · 2023Article
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.Scientific reports · 2023Article
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Authors and funding
13 authors.
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Abstract
We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
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