Convolutional Neural Network and Its Advances: Overview and Applications

Abstract

For the last few years, deep learning has become an area of interest for academicians and researchers. Convolutional neural network (CNN) is one of the deep neural networks that shows excellent performance in more areas than the classical machine learning methods and is the most powerful and popular deep necural network. CNN uses different building blocks like convolution layers, pooling layers, and fully connected layers to automatically learn features through backpropagations. This chapter focuses on basic concepts and understanding of CNN elements and commonly used CNN architectures and learning algorithms. Various parameters like activation function, loss function, regularization, optimization, number of layers, and layer design affect the performance of CNNs. Hence, we summarize recent enhancement of CNNs by considering all these aspects. CNN shows its excellence in areas like computer vision, natural language processing, speech processing, and radiology. Based on various scenarios, different dimensions of convolutions like one-dimensional, two-dimensional, and multi-dimensional CNN are used. This chapter details such applications of CNN in various domains. Further, the chapter discusses some common issues and future research directions in the field of CNN. The aim of this chapter is to provide a detailed understanding of CNN with its advances and applications, which will help researchers further develop applications in the field of CNN.