A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae Classification

TL;DR

This study compares deep learning architectures, including CNNs and Vision Transformers, for classifying mosquito larvae, finding ConvNeXT most effective, which aids in faster, more accurate mosquito species identification crucial for disease control.

Contribution

It introduces a comparative analysis of CNN and transformer-based models for mosquito larvae classification, highlighting ConvNeXT's superior performance and proposing future hybrid model development.

Findings

ConvNeXT achieved the highest classification metrics.

Transformer-based models show promise for mosquito image classification.

CNN models remain competitive in resource-constrained settings.

Abstract

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.