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VGG-CovidNet: Bi-Branched Dilated Convolutional Neural Network for Chest X-Ray-Based COVID-19 Predictions

Muhammed Binsawad1,*, Marwan Albahar2, Abdullah Bin Sawad1

1 Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
2 Department of Science, Umm Al Qura University, Mecca, Saudi Arabia

* Corresponding Author: Muhammed Binsawad. Email: email

(This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)

Computers, Materials & Continua 2021, 68(2), 2791-2806.


The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated bi-branched convoluted neural network (CNN) architecture, VGG-COVIDNet, to detect COVID-19 cases from CXR images. The front end of the VGG-COVIDNet consists of the first 10 layers of VGG-16, where the convolutional layers in these layers are reduced to two to minimize latency during the training phase. The last two branches of the proposed architecture consist of dilated convolutional layers to reduce the model’s computational complexity while retaining the feature maps’ spatial information. The simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and sensitivity. The proposed architecture’s accuracy and sensitivity are 96.5% and 96%, respectively, for each infection type.


Cite This Article

M. Binsawad, M. Albahar and A. Bin Sawad, "Vgg-covidnet: bi-branched dilated convolutional neural network for chest x-ray-based covid-19 predictions," Computers, Materials & Continua, vol. 68, no.2, pp. 2791–2806, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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