Incorporating self-attention into DenseNet for multi-label chest X-ray image classification

This paper presents DNet-nSA, a novel deep learning architecture designed to enhance multi-label classification of chest X-ray (CXR) images by integrating n self-attention blocks into the DenseNet framework. While convolutional neural networks (CNNs) are effective at identifying local patterns, they frequently face challenges in capturing long-range dependencies and global context, which are essential for detecting spatially distributed abnormalities in CXR images. By embedding self-attention mechanisms, DNet-nSA allows the network to better capture non-local interactions and highlight diagnostically relevant regions. We propose and evaluate two variants: DNet-1SA and DNet-2SA, corresponding to the number of self-attention modules used. Experiments conducted on the ChestX-ray14 dataset demonstrate that the proposed models outperform the baseline DenseNet, the contrastive learning approach MoCoR101, and the self-supervised learning model MoBYSwinT, achieving a notable AUC of 0.822, confirming the effectiveness of self-attention in improving multi-label CXR image classification.