Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

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Cannet, Arnaud | Simon-Chane, Camille | Akhoundi, Mohammad | Histace, Aymeric | Romain, Olivier | Souchaud, Marc | Jacob, Pierre | Sereno, Darian | Mouline, Karine | Barnabé, Christian | Lardeux, Frédéric | Boussès, Philippe | Sereno, Denis

Edité par CCSD ; Nature Publishing Group -

International audience. 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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