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

作     者：Muhammed Binsawad Marwan Albahar Abdullah Bin Sawad 

作者机构：Information SystemsKing Abdulaziz UniversityJeddahSaudi Arabia Department of ScienceUmm Al Qura UniversityMeccaSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第8期

页      面：2791-2806页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by Institutional Fund projects(Grant No.IFPHI-255-611-2020) 

主　　题：Coronavirus disease 2019 prognosis X-ray images deep learning artificial intelligence 

摘      要：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 *** studies have shown irregularities in the chest radiographs of COVID-19 *** 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 *** study introduces a dilated bi-branched convoluted neural network(CNN)architecture,VGG-COVIDNet,to detect COVID-19 cases from CXR *** 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 *** 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 *** simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and *** proposed architecture’s accuracy and sensitivity are 96.5%and 96%,respectively,for each infection type.