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

Abstract

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 learning to obtain the image‐level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k ‐nearest neighbors algorithm, in which the ILRs were linked with their k ‐nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end‐to‐end COVID‐19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state‐of‐the‐art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID‐19, which can be used in clinical diagnosis.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

“…Then, these 2 D images are applied to different 2D deep learning networks. Another future work is using novel DL techniques such as attention learning [119][120][121][122], transformers [123,124], and other advanced deep learning techniques [125][126][127][128][129][130][131][132][133][134] for epileptic seizure detection. Finally, adopting novel deep feature fusion techniques to epileptic seizures detection based on EEG signals can be noteworthy as one of the future works [135].…”

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model

“…To verify the overall effectiveness of TSRNet, we compared it with five models (DarkCOVIDNet [ 7 ], Deep-COVID [ 46 ], NAGNN [ 47 ], COVID-ResNet [ 48 ], Patch-based CNN [ 49 ]). The results are shown in Fig.…”

Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach

“…The process of recognizing different diseases and disorders is done by using detection methods, where one of the most impactful methods is computer-aided diagnosis algorithms [11] , [12] . After recognizing the illness, the process of controlling covid-19 spread is started.…”