期刊
NEUROCOMPUTING
卷 452, 期 -, 页码 592-605出版社
ELSEVIER
DOI: 10.1016/j.neucom.2020.07.144
关键词
COVID-19; Pneumonia; Graph neural network; ResGNet-C; Deep learning
资金
- Royal Society International Exchanges Cost Share Award, UK [RP202G0230]
- Medical Research Council Confidence in Concept Award, UK [MC_PC_17171]
- Hope Foundation for Cancer Research, UK [RM60G0680]
- British Heart Foundation Accelerator Award, UK
- Fundamental Research Funds for the Central Universities [CDLS202003]
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education
- Guangxi Key Laboratory of Trusted Software [kx201901]
- University of Leicester
A graph convolutional neural network ResGNet-C has been developed for automatically classifying lung CT images to accurately diagnose pneumonia caused by COVID-19. Experimental results demonstrate that the system's performance surpasses all state-of-the-art methods, and the proposed graph construction method is simple and effective.
The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-ofthe-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results. (c) 2020 Elsevier B.V. All rights reserved.
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