Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

作     者：Ruaa A.Al-Falluji Zainab Dalaf Katheeth Bashar Alathari 

作者机构：University of BabylonBabylon51002Iraq Kufa UniversityKufa54003Iraq 

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

年 卷 期：2021年第66卷第2期

页      面：1301-1313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] 

主　　题：COVID-19 artificial intelligence convolutional neural network chest x-ray images Resnet18 model 

摘      要：The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote *** this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal *** goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking *** Learning technique has been implemented in this *** learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical *** findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease *** all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the *** proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 *** on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.