ABSTRACT
In recent years, chest X-ray (CXR) imaging has become one of the significant tools to assist in the diagnosis and treatment of novel coronavirus pneumonia. However, CXR images have complex-shaped and changing lesion areas, which makes it difficult to identify novel coronavirus pneumonia from the images. To address this problem, a new deep learning network model (BoT-ViTNet) for automatic classification is designed in this study, which is constructed on the basis of ResNet50. First, we introduce multi-headed self-attention (MSA) to the last Bottleneck block of the first three stages in the ResNet50 to enhance the ability to model global information. Then, to further enhance the feature expression performance and the correlation between features, the TRT-ViT blocks, consisting of Transformer and Bottleneck, are used in the final stage of ResNet50, which improves the recognition of complex lesion regions in CXR images. Finally, the extracted features are delivered to the global average pooling layer for global spatial information integration in a concatenated way and used for classification. Experiments conducted on the COVID-19 Radiography database show that the classification accuracy, precision, sensitivity, specificity, and F1-score of the BoT-ViTNet model is 98.91%, 97.80%, 98.76%, 99.13%, and 98.27%, respectively, which outperforms other classification models. The experimental results show that our model can classify CXR images better. © 2022 by the authors.