Chest X-rays are used to diagnose a wide range of chest conditions. However, due to the complicated and diverse features of thoracic diseases, existing disease classification algorithms for chest radiographs have difficulty in learning the complex discriminating features of thoracic diseases and do not fully consider correlation information between different diseases. This study proposes a disease classification algorithm that combines self-attention and convolution to address these problems. This study employs omni-dimensional dynamic convolution to replace the standard convolution of the residual network to enhance the feature extraction capabilities of the network for multi-scale information. In addition, a self-attention module is introduced into the convolutional neural network to provide global receptive fields that capture correlations between multiple diseases. Finally, an efficient double path attention is proposed that allows the network to give greater attention to the focal area and automatic capturing of changes in lesion locations. The proposed model is evaluated on the ChestX-ray14 dataset. Experimental results show that the accuracy of the algorithm and the efficiency of diagnosis for the classification of 14 chest diseases is improved over those of the seven current state-of-the-art algorithms, with an average area under receiver operating characteristic curve (AUC) value of 0.839.