A Comprehensive Exploration of Convolutional Neural Network Architectures in Deep Learning

The fields of computer vision, natural language processing, and medical image analysis have all seen significant transformation because of deep learning’s convolutional neural networks (CNNs). This comprehensive review explores the extensive landscape of CNN-based techniques, providing a detailed analysis of their architecture, training strategies, and applications. The review starts by introducing the fundamental concepts of CNNs, emphasizing their ability to automatically learn hierarchical features from raw data, analysis of the different components, design choices that influence the performance of CNN architectures, including the choice of activation functions, pooling strategies, and network depth. It then delves into various CNN architectures, including LeNet, AlexNet, ZFNet, VGG, GoogleNet, ResNet, DenseNet, MobileNet, and UNet, outlining their unique structures and applications.