A Novel Technique for Early Detection of COVID-19

作     者：Mohammad Yamin Adnan Ahmed Abi Sen Zenah Mahmoud AlKubaisy Rahaf Almarzouki 

作者机构：Department of Management Information SystemsFaculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Faculty of Computer and Information SystemsIslamic University of MadinahAl MedinaSaudi 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：2283-2298页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University Jeddah under Grant No.GCV19-49-1441 

主　　题：COVID-19 classification feature selection dimensionality reduction chest CT deep learning deep convolutional neural network AIRRCNN 

摘      要：COVID-19 is a global pandemic disease,which results from a dangerous coronavirus attack,and spreads aggressively through close contacts with infected people and *** far,there is not any prescribed line of treatment for COVID-19 *** to control the disease are very limited,partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the *** detection of positive cases is critical in preventing further spread,achieving the herd immunity,and saving ***,so far we do not have effective toolkits to diagnose very early detection of the *** research findings have suggested that radiology images,such as X-rays,contain significant information to detect the presence of COVID-19 virus in early ***,to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not *** Intelligence(AI)techniques,machine learning in particular,are known to be very helpful in accurately diagnosing many diseases from radiology *** paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography(CT)scan *** technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network(AIRRCNN),which uses machine learning techniques for classifying *** focus on the Advanced Inception based Recurrent Residual Convolution Neural Network,because we do not find it being used in the ***,we conduct principal component analysis,which is used for dimensional *** results of our method have demonstrated an accuracy of about 99%,which is regarded to be very efficient.