Design and Evolution of Deep Convolutional Neural Networks in Image Classification – A Review

Abstract

Convolutional Neural Network(CNN) is a well-known computer vision approach successfully applied for various classification and recognition problems. It has an outstanding power to identify patterns in 1D and 2D data. Though invented in 80’s, it became hugely successful after LeCun’s work on digit identification. Several CNN based models have been developed to record splendid performance on ImageNet and other databases. Ability of the CNN in learning complex features at different hierarchy from the data had made it the most successful among deep learning algorithms. Innovative architectural designs and hyper parameteroptimization have greatly improved the efficiency of CNN in pattern recognition. This review majorly focuses on the evolution and history of CNN models. Landmark CNN architectures are discussed with their categorization depending on various parameters. In addition, this also explores the architectural details of different layers, activation function, optimizers and other hyperparameters used by CNN. Review concludes by shedding the light on the applications and observations to be considered while designing the network.