Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

Xiaoling Liang; Yuexin Zhang; Jiahong Wang; Qing Ye; Yanhong Liu; Jinwu Tong

doi:10.3389/fmed.2020.612962

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

Front Med (Lausanne). 2021 Jan 21:7:612962. doi: 10.3389/fmed.2020.612962. eCollection 2020.

Authors

Xiaoling Liang^{1

2}, Yuexin Zhang³, Jiahong Wang⁴, Qing Ye^{5

6}, Yanhong Liu⁷, Jinwu Tong⁸

Affiliations

¹ Department of Marine Engineering, Dalian Maritime University, Dalian, China.
² Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
³ School of Instrument Science and Engineering, Southeast University, Nanjing, China.
⁴ Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
⁵ Division of Life Sciences and Medicine, Department of Pathology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
⁶ Division of Life Sciences and Medicine, Intelligent Pathology Institute, University of Science and Technology of China, Hefei, China.
⁷ Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
⁸ School of Innovation and Entrepreneurship, Nanjing Institute of Technology, Nanjing, China.

Abstract

A three-dimensional (3D) deep learning method is proposed, which enables the rapid diagnosis of coronavirus disease 2019 (COVID-19) and thus significantly reduces the burden on radiologists and physicians. Inspired by the fact that the current chest computed tomography (CT) datasets are diversified in equipment types, we propose a COVID-19 graph in a graph convolutional network (GCN) to incorporate multiple datasets that differentiate the COVID-19 infected cases from normal controls. Specifically, we first apply a 3D convolutional neural network (3D-CNN) to extract image features from the initial 3D-CT images. In this part, a transfer learning method is proposed to improve the performance, which uses the task of predicting equipment type to initialize the parameters of the 3D-CNN structure. Second, we design a COVID-19 graph in GCN based on the extracted features. The graph divides all samples into several clusters, and samples with the same equipment type compose a cluster. Then we establish edge connections between samples in the same cluster. To compute accurate edge weights, we propose to combine the correlation distance of the extracted features and the score differences of subjects from the 3D-CNN structure. Lastly, by inputting the COVID-19 graph into GCN, we obtain the final diagnosis results. In experiments, the dataset contains 399 COVID-19 infected cases, and 400 normal controls from six equipment types. Experimental results show that the accuracy, sensitivity, and specificity of our method reach 98.5%, 99.9%, and 97%, respectively.

Keywords: 3D convolutional neural network; COVID-19; chest computed tomography; equipment types; graph convolutional network.