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IMAGE: Representative specimen image of Anopheles stephensi from Mosquito Image Database. Picture shows a mosquito stored by freezing at -80C.
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Credit: Couret, J. et al. 2020 (CC-BY 2.0)
Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of mosquito-borne disease surveillance. Now, researchers reporting in
PLOS Neglected Tropical Diseases have shown the effectiveness of an artificial intelligence system known as a Convoluted Neural Network to classify mosquito sex, genus, species and strain.
Human malaria is an ongoing public health crisis affecting multiple continents, with the highest numbers of cases and people at risk occurring in sub-Saharan Africa. However the identification of mosquitoes that transmit malaria can be difficult some species are nearly indistinguishable even to trained taxonomists. ....

United States , University Of Rhode Island , Convoluted Neural Network , National Institute Of Food , Research Article , Tropical Diseases , Convoluted Neural Network To , Jannelle Couret , Rhode Island , Hatch Regional Project , ஒன்றுபட்டது மாநிலங்களில் , பல்கலைக்கழகம் ஆஃப் ரோட் தீவு , தேசிய நிறுவனம் ஆஃப் உணவு , ஆராய்ச்சி கட்டுரை , வெப்பமண்டல நோய்கள் , ஜன்னெல்லே கூரேட் , ரோட் தீவு , ஹட்ச் பிராந்திய ப்ராஜெக்ட் ,