Research on classification of COVID-19 and common pneumonia by x-ray images based on convolutional attention mechanism

Yong Zhang; Zhengyang Sun

doi:10.1117/12.2641074

3 October 2022 Research on classification of COVID-19 and common pneumonia by x-ray images based on convolutional attention mechanism

Yong Zhang, Zhengyang Sun

Author Affiliations +

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 1229011 (2022) https://doi.org/10.1117/12.2641074
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

Abstract

As the COVID-19 pandemic spreads across the globe, it highlights the importance of using all available resources to mitigate this common human challenge. Therefore, this paper studies and evaluates the general convolutional neural network and a method based on lung X-ray image classification. Attention mechanism mechanism + DenesNet method for detecting infected patients from chest X-ray images. In order to improve the validity, the mixed data set is preprocessed in this paper. In order to reduce the problem of small number of samples, we adopt transfer learning to transfer the information extracted from the pre-trained model to the model to be trained. The experimental results show that the overall accuracy of the design experiment in this paper has been improved to a certain extent compared with the original network model, and the overall accuracy has reached 83.82%.

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Yong Zhang and Zhengyang Sun "Research on classification of COVID-19 and common pneumonia by x-ray images based on convolutional attention mechanism", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 1229011 (3 October 2022); https://doi.org/10.1117/12.2641074

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KEYWORDS

Data modeling

X-ray imaging

Image classification

Convolutional neural networks

Network architectures

Convolution

Statistical modeling

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