Application of convolutional neural networks for classification of adult mosquitoes in the field

• DOI: 10.1371/journal.pone.0210829
• Publication Date: 2019-01-14T18:31:44Z
• Authors: Daniel Motta; Alex Álisson Band...; Ingrid Winkler; Bruna Aparecida S...; Daniel André Dias...; Alexandre Morais ...; Eduardo Oyama Lin...; Frank Kirchner; Roberto Badaró
• Keywords: Male; Models, Anatomic; 0303 health sciences; Databases, Factual; Science; Q; R; Mosquito Vectors; Arbovirus Infections; 3. Good health; Machine Learning; Culex; 03 medical and health sciences; Species Specificity; Aedes; Image Processing, Computer-Assisted; Medicine; Animals; Humans; Female; Neural Networks, Computer; Research Article

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.