  Mosquito-borne diseases (MBDs), such as dengue virus, chikungunya virus, and
West Nile virus, cause over one million deaths globally every year. Because
many such diseases are spread by the Aedes and Culex mosquitoes, tracking these
larvae becomes critical in mitigating the spread of MBDs. Even as citizen
science grows and obtains larger mosquito image datasets, the manual annotation
of mosquito images becomes ever more time-consuming and inefficient. Previous
research has used computer vision to identify mosquito species, and the
Convolutional Neural Network (CNN) has become the de-facto for image
classification. However, these models typically require substantial
computational resources. This research introduces the application of the Vision
Transformer (ViT) in a comparative study to improve image classification on
Aedes and Culex larvae. Two ViT models, ViT-Base and CvT-13, and two CNN
models, ResNet-18 and ConvNeXT, were trained on mosquito larvae image data and
compared to determine the most effective model to distinguish mosquito larvae
as Aedes or Culex. Testing revealed that ConvNeXT obtained the greatest values
across all classification metrics, demonstrating its viability for mosquito
larvae classification. Based on these results, future research includes
creating a model specifically designed for mosquito larvae classification by
combining elements of CNN and transformer architecture.
