A bagging dynamic deep learning network for diagnosing COVID-19

• Published in: Scientific reports Vol. 11; no. 1; p. 16280
• Main Authors: Zhang, Zhijun, Chen, Bozhao, Sun, Jiansheng, Luo, Yamei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 692/699; Algorithms; Chest; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; Deep Learning; Diagnosis, Computer-Assisted; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted; Lung - diagnostic imaging; multidisciplinary; Pandemics; Radiography; Science; Science (multidisciplinary); Tomography, X-Ray Computed - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-95537-y

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.