Image classification is a fundamental task in computer vision that entails categorizing images into predefined classes according to their visual features. Traditional classification approaches mostly relied on handcrafted feature extraction techniques, which often struggled to handle complex patterns, high variability and large-scale diverse datasets. Recent advancements in deep learning have highly impacted image classification tasks by enabling automatic hierarchical feature learning directly from raw image data. This review presents a comprehensive analysis of advanced deep learning architectures designed for image classification, focusing on architectural innovations, feature representation, transfer learning strategies and model interpretability. Different network architectures, such as residual networks, recurrent attention-based networks, fully convolutional networks, capsule networks and regionbased convolutional networks are critically reviewed based on their design and performance across different application domains. The review further explores the role of pre-trained models and knowledge transfer techniques in addressing challenges, data scarcity and training complexity. In addition, it discusses widely used development frameworks that facilitate efficient model implementation and deployment. By integrating both theoretical knowledge and recent developments, this review provides systematic guidance for researchers and practitioners to develop efficient, scalable and robust image classification systems for both scientific research and industrial applications.
Gogoi et al. (Fri,) studied this question.