CSAERNet: An Efficient Deep Learning Architecture for Image Classification

Deep learning performs a great role in solving many problems, particularly with image classification. Still, the performance of many architectures is often limited at some points, especially with regular architecture. In this paper, we propose an efficient and noncomplex image classification architecture using deep learning based on the most popular algorithms: Convolutional Neural Network (CNN) for Feature extraction, Stacked AutoEncoder (SAE) for reducing the dimensionality, and Recurrent Neural Network (RNN) for increasing the accuracy. The proposed network, called Convolutional Stacked AutoEncoder Recurrent Neural Network (CSAERNet), that takes the input to be features-extracted by CNN then put as input to SAE for dimensionality reduction after that the RNN takes it as input to increase the accuracy. The proposed CSAERNet has been evaluated on two widely-used benchmark datasets; CIFAR-10 and CIFAR-100. The obtained results are compared with the latest state-of-the-art approaches, the results showed that the proposed CSAERNet perform well on both datasets and outperforms on other models.