A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

作     者：Aghila Rajagopal Sultan Ahmad Sudan Jha Ramachandran Alagarsamy Abdullah Alharbi Bader Alouffi 

作者机构：Department of Computer Science and Business SystemsSethu Institute of TechnologyKariapattiVirudhunagar626115TamilnaduIndia Department of Computer ScienceCollege of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science and EngineeringSchool of EngineeringKathmandu UniversityBanepaKathmanduNepal Department of CSEUniversity College of EngineeringPanrutiTamilnaduIndia Department of Information TechnologyCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第45卷第6期

页      面：3215-3229页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supporting this research through Taif University Researchers Supporting Project number(TURSP-2020/231) Taif University Taif Saudi Arabia 

主　　题：Covid-19 CT images multi-scale improved ResNet AI inception 14 and VGG-16 models 

摘      要：Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.