Machine Learning & Data Science Research

URI Researchers Trained CNN Networks to ID mosquitos

Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of mosquito-borne disease surveillance.

Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of mosquito-borne disease surveillance. On November 17, Jannelle Couret’s team from University of Rhode Island have shown the effectiveness of convolutional neural network (CNN) to classify mosquito sex, genus, species and strain, with results published on PLOS Neglected Tropical Diseases.

The team applied a convoluted neural network to a library of 1,709 two-dimensional images of adult mosquitos. The mosquitoes were collected from 16 colonies in five geographic regions and included one species not readily identifiable to trained medical entomologists. They also included mosquitoes that had been stored in two different ways – by flash freezing or as dried samples.

Using the library of identified species, the researchers trained the CNN to distinguish Anopheles from other mosquito genera, to identify species and sex within Anopheles, and to identify two strains within a single species. They found a 99.96% prediction accuracy for class and a 98.48% accuracy for sex. (Source)


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1 comment on “URI Researchers Trained CNN Networks to ID mosquitos

  1. Very useful information. Thank you very much.

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