Certain species of mosquitoes are the main vectors of arboviruses that cause Dengue, Yellow fever, Chikungunya, Zika and Japanese encephalitis. These species are contained in the genera Anopheles, Culex, Aedes. Mosquito-borne diseases pose significant threat to public health. Therefore, vector surveillance and vector control strategies are crucial. Automation of genera identification is essential to implement effective vector control strategies. In the past decade several machine learning and deep learning models have been investigated for image-based automatic and accurate classification of vector mosquitoes. Such applications also aid entomologists in insect identification task. In this study, a deep convolutional neural network technique to classify two genera of mosquitoes: Aedes and Culex based on the morphological features is proposed. In our work, an optimization technique i.e., transfer learning using the pretrained deep learning models has been employed. Transfer learning saves the model training time and addresses the problem of low performance due to insufficient amount of training data. This paper presents the architecture of three state of the art pretrained neural networks, including VGGNet, ResNet and GoogLeNet. The models were trained with our own dataset of images of the two genera of mosquitoes. Classification performance of the models is evaluated in terms of classification accuracy and loss during training and validation phases of model building.