Image Classification Using Deep Learning: A Systematic Review

Abstract

Image classification is a fundamental problem in the field of computer vision and involves assigning a label to an image based on its content. In this paper, we survey both traditional machine learning-based image classification methods and deep learning-based image classification methods, specifically, we review two deep learning-based image classification methods: a convolutional neural network (CNN) method and a pre-trained CNN-based transfer learning method for image classification. We first briefly review traditional machine learning-based image classification methods and then deep learning-based image classification methods, discussing both the feature extraction methods and the classification methods used in the deep learning-based methods. We discuss several deep neural network architectures for image classification, including LeNet, AlexNet, VGGNet, GoogleNet, ResNet, and DenseNet, and finally, we conclude with a discussion on the applications of image classification and compare the different methods based on various factors both qualitatively and quantitatively, and present our experimental results, while also summarizing various methods of data augmentation, batch normalization, and regularization by dropout. We introduce techniques for practical training of deep networks, and discuss fine-tuning, pruning, and model quantization for efficient inference of trained neural networks, and finally, we introduce and discuss the runtime engines for the deployment of trained neural networks for efficient inference.