TY - JOUR
T1 - Cgenet
T2 - A deep graph model for covid-19 detection based on chest ct
AU - Lu, Si Yuan
AU - Zhang, Zheng
AU - Zhang, Yu Dong
AU - Wang, Shui Hua
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Computer-aided diagnosis
KW - Convolutional neural network
KW - Extreme learning machine
KW - Feedforward neural network
KW - Graph neural network
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85121857707&partnerID=8YFLogxK
U2 - 10.3390/biology11010033
DO - 10.3390/biology11010033
M3 - Article
AN - SCOPUS:85121857707
SN - 2079-7737
VL - 11
JO - Biology
JF - Biology
IS - 1
M1 - 33
ER -