NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network

• Published in: International journal of intelligent systems Vol. 37; no. 2; pp. 1572 - 1598
• Main Authors: Lu, Siyuan, Zhu, Ziquan, Gorriz, Juan Manuel, Wang, Shui‐Hua, Zhang, Yu‐Dong
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2022; John Wiley and Sons Inc
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Casualties; Classification; Computed tomography; convolutional neural network; COVID-19; Diagnosis; Economic impact; graph convolutional network; Graph neural networks; Graphical representations; Intelligent systems; Medical imaging; Neural networks; random vector functional link net; transfer learning; graph convolutional network; transfer learning; random vector functional link net; convolutional neural network

COVID‐19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer‐aided diagnosis system based on artificial intelligence to automatically identify the COVID‐19 in chest computed tomography images. We utilized transfer...