CONVOLUTIONAL NEURAL NETWORKS: ARCHITECTURAL FOUNDATIONS, EVOLUTION, AND APPLICATIONS IN MODERN COMPUTER VISION

Abstract

Convolutional Neural Networks (CNNs) have revolutionized computer vision by enabling automatic hierarchical feature learning from raw pixel data, leading to state-of-the-art performance in image classification, object detection, and segmentation. This review synthesizes the architectural foundations of CNNs, emphasizing their multi-layered abstraction capabilities and the advantages of hierarchical deep CNNs for complex classification tasks. We discuss dimensional adaptations—1D, 2D, and 3D CNNs—and highlight their application domains, computational characteristics, and output structures. Comparative analysis demonstrates that CNNs outperform traditional artificial neural networks (ANNs) and recurrent neural networks (RNNs) in spatial data tasks, and, while Vision Transformers (ViTs) excel in large-scale settings, CNNs remain more data-efficient and computationally practical for many real-world applications. Despite these strengths, standard CNNs are more susceptible to high levels of image noise compared to human observers; however, targeted training with blurred or noisy images significantly narrows this gap, improving robustness and aligning network behavior more closely with human perception. Ongoing research into hybrid architectures and advanced training protocols continues to address challenges in interpretability, efficiency, and adaptability, ensuring CNNs remain at the forefront of deep learning innovation.