Convolutional Neural Networks (CNNs) have emerged as a cornerstone in the field of deep learning, demonstrating remarkable performance across various domains, including computer vision and natural language processing. Their widespread acceptance on both academic and industrial levels has spurred much research and development. This study provides a comprehensive overview of recent developments in convolutional neural network (CNN) architectures by analyzing their foundational concepts, structural enhancements, and various applications such as image classification, medical imaging, and autonomous systems. Starting with conventional CNN architectures and their fundamental elements, the paper uses a systematic approach to look into recent advances. Key architectural innovations, including advanced activation functions, novel pooling strategies, and optimized convolutional techniques, are discussed. The study also explores hybrid architectures that integrate CNNs with transformers and recurrent neural networks to enhance contextual and sequential learning. Advances in training processes, such as enhanced loss functions and regularization approaches, have been studied for enhancing model performance. The study highlights CNN advancements that improve accuracy, reduce computational costs, and enhance model generalization. It underscores the effectiveness of CNNs in critical domains. Findings reveal that improved feature extraction techniques and interpretability methods, including Gradient-weighted Class Activation Mapping (Grad-CAM) and Graph-CNNs, contribute significantly to CNN performance. Finally, the paper identifies open challenges and outlines potential research directions, providing insights into the future development of CNNs.
Reza et al. (Sun,) studied this question.