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

• Published in: Biology (Basel, Switzerland) Vol. 11; no. 1; p. 33
• Main Authors: Lu, Si-Yuan, Zhang, Zheng, Zhang, Yu-Dong, Wang, Shui-Hua
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.12.2021; MDPI
• Subjects: Accuracy; Algorithms; Chest; Classification; Computed tomography; computer-aided diagnosis; convolutional neural network; Coronaviruses; COVID-19; COVID-19 infection; data collection; Datasets; Deep learning; Diagnosis; Disease transmission; Embedding; Experiments; extreme learning machine; feedforward neural network; graph neural network; Learning algorithms; Machine learning; mathematical theory; Medical research; Methods; Neural networks; Pneumonia; Transfer learning; transfer learning; feedforward neural network; graph neural network; computer-aided diagnosis; convolutional neural network; extreme learning machine
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology11010033

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.