Use of Neural Networks for Image Classification

Abstract

Neural networks have become the foundation of modern image classification systems, enabling significant advances in computer vision. Their ability to learn hierarchical representations directly from raw pixel data allows them to outperform traditional machine learning approaches. This article provides an overview of the principles, architectures, training techniques, and challenges associated with applying neural networks to image classification tasks. We analyze commonly used models, discuss optimization strategies, and highlight trends shaping the future of neural image classification.

Topics

Identifiers

DOI

10.5281/zenodo.17845289

Citations and references