Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

The paper is a comprehensive review of deep learning (DL), which it describes as the gold standard and most widely used computational approach in machine learning, achieving outstanding results on complex cognitive tasks and often matching or beating human performance. Observing that prior surveys each tackled only one aspect of DL and thus left an overall lack of knowledge, the authors take a more holistic approach: they outline the importance of DL, present the types of DL techniques and networks, and give special attention to convolutional neural networks (CNNs) as the most utilized network type, describing the development of CNN architectures from AlexNet through to the High-Resolution network (HRNet).

Beyond architectures, the review presents challenges in DL together with suggested solutions to help researchers identify existing research gaps, followed by a list of major DL applications across domains such as cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing. It summarizes computational tools including FPGA, GPU, and CPU and their influence on DL, and closes with an evolution matrix, benchmark datasets, and a summary, aiming to provide a suitable starting point for developing a full understanding of the field.