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COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.

COVID-19 is a serious epidemic all over the world. As an efficient way in intelligent medical services, using X-ray chest radiography image for automatically diagnosing COVID-19 provides huge assistances and conveniences for clinicians in practice. In this paper, a bagging dynamic deep learning network (B-DDLN) is proposed for diagnosing COVID-19 by intelligently recognizing X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects feature extractor and bagging classifier in series. Experimental results verify that using the proposed B-DDLN can achieve 98.8889% testing accuracy, which illustrates the best diagnosis performances among existing methods on the open image set and provides evidences for further detection and treatment.

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