In this new study, researchers demonstrated the effectiveness of an artificial intelligence (AI) system – known as a Convoluted Neural Network – for classifying the sex, genus, species and strain of mosquitoes.
Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of monitoring mosquito-borne diseases, according to the study published in PLOS Neglected Tropical Diseases.
Human malaria is an ongoing public health crisis affecting many continents. Sub-Saharan Africa has the highest number of cases and people at risk.
However, identifying mosquitoes that transmit malaria can be difficult – some species are difficult to distinguish, even for trained taxonomists.
In the new work, the research team at the University of Rhode Island in the US and colleagues used a Convoluted Neural Network (CNN) for a library of 1,709 two-dimensional images of adult mosquitoes.
The mosquitoes were collected from 16 colonies in five geographic regions and included a species that was not easily identified by trained medical entomologists.
This included mosquitos stored in two different ways – by flash freezing or as dried samples.
Using the library of identified species, researchers trained the CNN to distinguish Anopheles from other mosquito genera, identify species and sex within Anopheles, and identify two phyla within a single species.
They found a prediction accuracy of 99.96 percent for the class and an accuracy of 98.48 percent for the gender.
“These results show that image classification using deep learning can be a useful method for identifying malaria mosquitoes, even in species with cryptic morphological variation,” the researchers said.
“Developing an independent and accurate method of identifying species can potentially improve mosquito surveillance practices,” they noted.
* Adapted from an IANS report