Understanding Convolutional Neural Networks for Image Classification

In an era where artificial intelligence is becoming increasingly globalized, the development of AI-based image processing has undergone a significant transformation compared to previous generations of mechanical algorithms. Whether compared to classical machine learning classification models or traditional image classification methods that do not involve machine learning, technologies based on Convolutional Neural Networks (CNNs) demonstrate notable advantages—including higher accuracy, stronger feature extraction capabilities, and broader cross-domain applicability. These benefits have opened new and viable pathways for the deployment of CNNs across various fields. However, no algorithm is perfect. With continued advancements in research, new challenges are constantly being discovered and addressed, contributing to the ongoing refinement of the CNN framework. Today, the concepts of CNNs and deep CNNs (DCNNs) have been widely integrated into a range of popular AI applications. This paper aims to provide a comprehensive review of classical CNN-based image classification models, examine the strengths and limitations present in various architectures, and further explore their potential future applications and developmental trajectories.