Research on COVID-19 pneumonia diagnosis based on chest x-ray (CXR) images using transformer and CNN

Ruofan Wu

doi:10.1117/12.2643477

9 October 2022 Research on COVID-19 pneumonia diagnosis based on chest x-ray (CXR) images using transformer and CNN

Ruofan Wu

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122461F (2022) https://doi.org/10.1117/12.2643477
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

Since December 2019, some hospitals in Wuhan, Hubei Province have found many cases of unidentified pneumonia, which were later named COVID-19. In recent years, the disease has spread widely all over the world. Many researchers are trying many methods to make rapid discovery and diagnosis for COVID-19, which is important to facilitate the treatment of the disease and to reduce its transmission. In this study, we propose the comparison of methods for COVID-19 pneumonia diagnosis based on chest X-ray (CXR) images using Transformer and CNN. Both of them are two of the most commonly studied deep learning method. In this paper, all four models will be trained with the COVID-19 Chest X-ray Database and tested for the performance on the four-class classification. The results showed that Swin Transformer achieved the highest score in the comparison of accuracy as 0.916. Additionally, VGG as one of CNN architectures performs well on the recognition of COVID-19 positive. This study can help doctors reduce their workload and effectively improve the speed and accuracy of detection of COVID-19 and other viral pneumonia.

Citation Download Citation

Ruofan Wu "Research on COVID-19 pneumonia diagnosis based on chest x-ray (CXR) images using transformer and CNN", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122461F (9 October 2022); https://doi.org/10.1117/12.2643477

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KEYWORDS

Transformers

Chest imaging

Data modeling

Visual process modeling

Lung

Opacity

Convolutional neural networks

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