Enhancing Image Classification Accuracy Using Attention Mechanisms in Convolutional Neural Networks

Abstract

Deep learning has revolutionized the field of image classification, with convolutional neural networks (CNNs) being at the forefront of this advancement. However, traditional CNNs often struggle with efficiently capturing long-range dependencies and focusing on the most relevant features of an image. To address these challenges, this paper proposes an enhanced CNN model incorporating attention mechanisms to improve classification accuracy. By integrating self-attention layers and a hybrid spatial-channel attention module, the model selectively focuses on the critical regions and features of images, leading to better representation learning. Experimental results on standard image classification datasets, such as CIFAR-10 and ImageNet, demonstrate a significant improvement in classification performance, with the proposed model outperforming several state-of-the-art architectures. The findings suggest that attention mechanisms can be a powerful tool to further advance deep learning models, particularly in tasks that require fine-grained feature extraction and interpretation.