Deep Learning for Image Classification: A Review of CNN-Based Approaches and Applications

Abstract

Convolutional neural networks (CNNs) constitute a powerful infrastructure for deep learning research. Widely adopted in the field of image recognition, CNNs represent a relatively recent technological advancement in China. Their primary advantage lies in feature extraction and modeling, which requires simulating the cognitive processes of the human brain across complex hierarchical levels, as well as accounting for visual perception differences. This capability increasingly bridges the functional relationship between robotic systems and human cognition. This paper provides a concise overview of CNNs and their applications in image recognition, image classification, information extraction, behavior detection, and behavior prediction within information processing systems. The analysis reveals current limitations in CNN-based approaches, including deficiencies in recognition accuracy, computational efficiency, methodological robustness, strategic frameworks, and feature extraction capabilities. Building upon these identified shortcomings, this paper synthesizes how contemporary domestic research has advanced the field through targeted improvements. The objective is to contribute to the understanding and tracking of progress in CNN research within China.