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

COVID-19 pneumonia started in December 2019 and caused large casualties huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence automatically identify the chest computed tomography images. We utilized transfer learning obtain image-level representation (ILR) backbone deep convolutional neural network. Then, novel neighboring aware (NAR) was proposed exploit relationships between ILR vectors. To information feature space of ILRs, an graph generated k-nearest neighbors algorithm, which ILRs were linked with their ILRs. Afterward, NARs by fusion graph. On basis representation, end-to-end classification architecture called network (NAGNN) proposed. The private public data sets used for evaluation experiments. Results revealed that our NAGNN outperformed all 10 state-of-the-art methods terms generalization ability. Therefore, is effective detecting COVID-19, can be clinical diagnosis.