A Bagging Dynamic Deep Learning Network for Diagnosing COVID-19

Abstract

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.

Analyzing the Therapeutic Mechanism of Mongolian Medicine Zhonglun-5 in Rheumatoid Arthritis Using a Bagging Algorithm with Serum Metabonomics

“…Furthermore, in research addressing novel Coronavirus-19 (COVID-19) diagnosis, Zhang et al proposed a bagging dynamic deep learning network (B-DDLN) to diagnose COVID-19 using intelligent recognition of chest symptoms in X-ray images. Te calculation results showed that the accuracy of B-DDLN was 98.8889%, and B-DDLN had the best diagnostic performance among the existing open image set diagnostic methods [9]. If the bagging algorithm is applied to the classifcation of metabonomics data, it can not only signifcantly improve the accuracy of metabonomics data classifcation but also be of great signifcance for expanding existing classifcation methods.…”