Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks

Abstract

Image-based automatic classification of vector mosquitoes has been investigated for decades for its practical applications such as early detection of potential mosquitoes-borne diseases. However, the classification accuracy of previous approaches has never been close to human experts' and often images of mosquitoes with certain postures and body parts, such as flatbed wings, are required to achieve good classification performance. Deep convolutional neural networks (DCNNs) are state-of-the-art approach to extracting visual features and classifying objects, and, hence, there exists great interest in applying DCNNs for the classification of vector mosquitoes from easy-to-acquire images. In this study, we investigated the capability of state-of-the-art deep learning models in classifying mosquito species having high inter-species similarity and intra-species variations. Since no off-the-shelf dataset was available capturing the variability of typical field-captured mosquitoes, we constructed a dataset with about 3,600 images of 8 mosquito species with various postures and deformation conditions. To further address data scarcity problems, we investigated the feasibility of transferring general features learned from generic dataset to the mosquito classification. Our result demonstrated that more than 97% classification accuracy can be achieved by fine-tuning general features if proper data augmentation techniques are applied together. Further, we analyzed how this high classification accuracy can be achieved by visualizing discriminative regions used by deep learning models. Our results showed that deep learning models exploit morphological features similar to those used by human experts. Mosquitoes cause global infectious disease burden, as vectors of numerous fatal diseases like malaria and dengue thrive by climate change and insecticide resistance. For example, in 2017, an estimated 219 million cases of malaria occurred and an estimated 435,000 deaths from malaria globally 1. For this reason, there have been significant efforts to develop a surveillance system for early detection and diagnosis of potential outbreaks of mosquito-borne diseases. Although, mosquito monitoring programs have been intensively developed worldwide, current mosquito monitoring procedures have many limitations. In particular, it takes at least a few days to detect potential pathogens of mosquito-borne diseases. Furthermore, one of the major bottlenecks in mosquito monitoring is that even classification by human experts of collected mosquitoes is a laborious and time-consuming procedure. Traditionally, trained researchers or technicians classify the species of mosquitoes by visual examination of morphological keys that provide step-by-step instructions on taxonomic characteristics of a given mosquito species 2. However, the number of taxonomists and classification experts has drastically decreased so far 3. Therefore, alternative automatic identification methods with expert-level classification accuracy are highly required in this field. Aut...

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