Cgenet: A deep graph model for covid-19 detection based on chest ct

Si Yuan Lu, Zheng Zhang, Yu Dong Zhang*, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


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.

Original languageEnglish
Article number33
Issue number1
Publication statusPublished - Jan 2022
Externally publishedYes


  • Computer-aided diagnosis
  • Convolutional neural network
  • Extreme learning machine
  • Feedforward neural network
  • Graph neural network
  • Transfer learning


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