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Article

CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT

by 1,†, 2,3,†, 1,* and 1,*
1
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
2
Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen 518055, China
3
Department of Computer and Information Science, University of Macau, Macau 999078, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Andreas Stadlbauer
Biology 2022, 11(1), 33; https://doi.org/10.3390/biology11010033
Received: 28 November 2021 / Revised: 23 December 2021 / Accepted: 25 December 2021 / Published: 27 December 2021
This study proposes a new COVID-19 detection system called CGENet, based on computer vision and chest computed tomography images. First, an optimal backbone selection algorithm was proposed to determine the best backbone network for the CGENet adaptively. Then, we introduced a novel graph embedding mechanism to fuse the spatial relationship into the feature vectors. Finally, we chose the extreme learning machine as the classifier of the proposed CGENet to boost the classification performance. The proposed CGENet was evaluated on a public dataset using 5-fold cross-validation and compared with other algorithms. The results revealed that the proposed model achieved state-of-the-art classification performance. In all, the CGENet can be an effective and efficient tool that can assist COVID-19 diagnosis.
Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis. View Full-Text
Keywords: computer-aided diagnosis; transfer learning; convolutional neural network; feedforward neural network; extreme learning machine; graph neural network computer-aided diagnosis; transfer learning; convolutional neural network; feedforward neural network; extreme learning machine; graph neural network
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MDPI and ACS Style

Lu, S.-Y.; Zhang, Z.; Zhang, Y.-D.; Wang, S.-H. CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT. Biology 2022, 11, 33. https://doi.org/10.3390/biology11010033

AMA Style

Lu S-Y, Zhang Z, Zhang Y-D, Wang S-H. CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT. Biology. 2022; 11(1):33. https://doi.org/10.3390/biology11010033

Chicago/Turabian Style

Lu, Si-Yuan, Zheng Zhang, Yu-Dong Zhang, and Shui-Hua Wang. 2022. "CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT" Biology 11, no. 1: 33. https://doi.org/10.3390/biology11010033

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