Deep Learning Techniques for Image Classification: Architectures, Advances, and Open Challenges
DOI:
https://doi.org/10.31033/IJEMR/16.3.2026.1929Keywords:
Computer Vision Image Classification, Deep Learning(DL), Convolutional Neural Networks(CNN)Abstract
Image classification has become a key task in computer vision. This shift is driven by rapid advancements in deep learning. Over the past decade, deep neural networks, especially Convolutional Neural Networks (CNNs), have greatly outperformed traditional machine learning methods. They do this by automatically learning feature representations from raw image data. More recently, new approaches like Vision Transformers (ViTs), hybrid architectures, and self-supervised learning methods have pushed the limits of classification performance across various datasets and applications. This paper offers a review of deep learning techniques for image classification, focusing on innovations in architecture, key milestones, and recent progress. We examine popular model families, including CNN-based architectures, transformer-based models, and lightweight networks suited for environments with limited resources. Additionally, we look at important training strategies, datasets, and evaluation metrics that have influenced the field. Despite significant advancements, several challenges persist. These include problems with model interpretability, stability against adversarial attacks, dependence on data, high computational needs, and the ability to generalize across different domains. This review highlights these ongoing challenges and suggests possible future research directions. We aim to give researchers and practitioners a clear understanding of the current state and future possibilities of deep learning in image classification.
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Research Articles in 'International Journal of Engineering and Management Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
