CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen 518055, China
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
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.