Identification of Vector and Non-vector Mosquito Species Using Deep Convolutional Neural Networks with Ensemble Model

Abstract

Human life has always been suffering from insects, particularly mosquitoes since its early beginnings. This annoying insect acts as a vector that transmits pathogens by feeding on our blood, spreading critical diseases like Zika Virus, Malaria, dengue fever, Chikungunya, etc. It's important to stop these dipterous insects from harming humans and need a method to identify the vector species. For many years, image-based automated identification of vector mosquitoes has been studied for applications such as early identification of mosquito-borne diseases. Here Deep Convolutional Neural Networks (DCNNs) are modern-day techniques for extracting visible functions and classifying objects and, there exists an excellent application for the classification of images. In this study, we analyzed the functionality of deep learning models in classifying mosquito species having excessive inter-species similarity and intra-species variations. We constructed a data set with approximately 3600 images of eight mosquito species with diverse postures and deformation conditions. Our result demonstrated that more than 98 % classification accuracy has been achieved by using our proposed ensemble method on this data. We also showed the comparison of various DCNNs models such as VGG-16, Inception V3, and MobileNetV2. The overall results show that InceptionV3 is the best model with 99.38% of training accuracy and 97.02% of testing accuracy.