Exploring the Impact of Recurrent Neural Networks on Image Classification and Optimizing Neural Configuration

Abstract

Abstract In this research, we delve into the unexplored potential of Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) networks in the field of Computer Vision (CV). With the trend of Deep Learning methods shifting towards complex Neural Network architectures, researchers have acknowledged the efficacy of Dense and Convolutional Neural Networks (CNNs) in CV tasks, while Recurrent Neural Networks (RNNs) have been proven to be effective in Natural Language Processing (NLP) problems. Despite some recent developments in blending RNN with CNN for NLP, there have been very few attempts in applying RNN-based approaches in CV. To address this gap, we take a unique in order to develop LSTM and GRU models by tuning the number of layers and number of neurons in each layer. The models are then applied to multiple benchmark datasets (Fashion-MNIST, MNIST, EMNIST-Letters, EMNIST-Digits, Kuzushiji-MNIST, and Kuzushiji-49). Our experimental results indicate that LSTM and GRU networks significantly outperforms corresponding CNN and Dense networks with optimal number of parameters. Our findings suggest that RNN (LSTM and GRU) can be valuable alternatives to CNN and Dense networks for image classification problems in CV. This study presents a new direction for further research in the field and highlights the potential of LSTM and GRU networks in CV tasks.