TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model

Abstract

Mosquitoes, which belong to diverse species, play a significant role in ecological systems and public health. The accurate identification (classification) of mosquito species is essential for a comprehensive understanding of their ecological roles, behaviors, and evolutionary patterns. While numerous studies have attempted to classify the mosquito species based on images, the existing works still have limitations. Our research is focused on vector mosquito classification based on deep ensemble transfer learning. Initially, we employed transfer learning via four pre-trained convolutional neural network (CNN) models. Subsequently, we have proposed the TransembleNet (Transfer Learning-based Ensemble Networks) approach, which is a novel method of generating ensemble learning models using four different combinations of three transfer learning models. All the experiments were done using the Nadam and Adam optimizers, and we have also applied data augmentation techniques. Among the four ensemble models, Ensemble Model 2 (composed of InceptionV3, VGG-16, and ResNet-50) performed the best. It exhibits very high precision, recall, F1-score, and accuracy values on the “Mosquito on Human Skin” dataset by Ong and Ahmed and the “Vector Mosquito” dataset by Park et al. Our proposed method outperformed the state-of-the-art research works for both datasets.