An improved COVID-19 classification model on chest radiography by dual-ended multiple attention learning

As a highly contagious disease, COVID-19 has not only had a great impact on the life, study and work of hundreds of millions of people around the world, but also had a huge impact on the global health care system. Therefore, any technical tool that allows for rapid screening and high-precision diagnosis of COVID-19 infections can be of vital help. In order to reduce the burden on health care system, the computer-aided diagnosis of COVID-19 has become a current research hotspot. X-ray imaging is a common and low-cost tool that can help with the COVID-19 diagnosis. The data used for this study has 15,153 CXR images, containing 10,192 normal lungs, 3,631 COVID-19 positive cases and 1,345 images of viral pneumonia. For this computer-aided task, we propose the dual-ended multiple attention learning model (DMAL). The model incorporates multiple attention learning into both networks, and the two networks are linked using an integration module. Specifically, in both networks, the backbone network is used to extract global features and the branch network captures local area information; the integration module combines multi-stage features; and the attention module containing element, channel and spatial attention prompts the model to focus on multi-scale information relevant to the disease. We evaluate the proposed DMAL network using relevant competitive methods as well as ten advanced deep learning models in the image domain and obtain the best performance with 99.67%, 99.53%, 99.66%, 99.60% and 99.76% in terms of Accuracy, Precision, Sensitivity, F1 Scores and Specificity. The proposed method will help in the rapid screening and high-precision diagnosis of COVID-19, given the general trend of such severe global infections. Our code and model are available in [https://github.com/Graziagh/DMALNet].