Deep Learning Architectures for Pattern Recognition: A Comparative Review of Challenges, Applications, and the Path Toward XAI

The recent rapid growth of deep learning has significantly reshaped the landscape of computer vision, establishing itself as the preferred paradigm for various tasks. Deep learning methods have demonstrated superior performance compared to previous state-of-the-art machine learning techniques across various fields. This review provides a concise overview of artificial neural networks (ANNs) and some of the most significant deep learning architectures, such as recurrent neural networks (RNNs), generative adversarial networks (GANs) and radial basis function networks (RBFNs). This review not only outlines the historical context and structures of these architectures but also provides a sophisticated understanding of their applications across different computer vision domains. A rigorous and comprehensive overview of these architectures is discussed throughout this review, and an essential systematic comparative analysis based on specific benchmarking criteria is provided. While individual deep learning frameworks excel in distinct domains, selecting the optimal architecture requires a balanced trade-off between algorithmic complexity, computational overhead, data dependencies, and structural interpretability. An intuitive and holistic benchmarking process synthesizes the core characteristics, technical configurations, operational constraints, and developmental pathways toward Explainable AI (XAI) and Green AI sustainability for the examined architectures (ANNs, RNNs, LSTMs, GANs, and RBFNs). Additionally, in this work the advantages and limitations of these architectures are discussed. Furthermore, an investigation of their applications in diverse computer vision tasks is carried out.