  Monitoring the spread of disease-carrying mosquitoes is a first and necessary
step to control severe diseases such as dengue, chikungunya, Zika or yellow
fever. Previous citizen science projects have been able to obtain large image
datasets with linked geo-tracking information. As the number of international
collaborators grows, the manual annotation by expert entomologists of the large
amount of data gathered by these users becomes too time demanding and
unscalable, posing a strong need for automated classification of mosquito
species from images. We introduce the application of two Deep Convolutional
Neural Networks in a comparative study to automate this classification task. We
use the transfer learning principle to train two state-of-the-art architectures
on the data provided by the Mosquito Alert project, obtaining testing accuracy
of 94%. In addition, we applied explainable models based on the Grad-CAM
algorithm to visualise the most discriminant regions of the classified images,
which coincide with the white band stripes located at the legs, abdomen, and
thorax of mosquitoes of the Aedes albopictus species. The model allows us to
further analyse the classification errors. Visual Grad-CAM models show that
they are linked to poor acquisition conditions and strong image occlusions.
