Diagnosis of COVID-19 Infected Lungs from Chest X-Ray Images

T. R. Niloy, A. Rahman
{"title":"Diagnosis of COVID-19 Infected Lungs from Chest X-Ray Images","authors":"T. R. Niloy, A. Rahman","doi":"10.53799/ajse.v20i1.142","DOIUrl":null,"url":null,"abstract":"Severe Acute Respiratory Symptom Coronavirus 2 (SARS-CoV-2) was newly discovered as a beta coronavirus. The virus-induced unexplained etiological pneumonia and is referred to as the 2019 Coronavirus Disease (COVID-19). Though the disease has appeared in a new way, there is no medication for transited patients. So, for diagnosing the COVID-19 infected lungs from X-Ray images, an automated technique has been suggested in this manuscript. In this study, Convolutional neural network (CNN) and VGG19 were used and found accuracy scores of 97% and 67%, respectively. The comparative analysis shows that the proposed method performs better than the solution that exists. Eventually, Precision, Recall, and F1-Score have been extracted and interpreted the model's loss functions in the research. This research has carried out by focusing on essential aspects in terms of COVID-19. Therefore, for the diagnosis of coronavirus infection, the technique can be used effectively.","PeriodicalId":224436,"journal":{"name":"AIUB Journal of Science and Engineering (AJSE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIUB Journal of Science and Engineering (AJSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53799/ajse.v20i1.142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Severe Acute Respiratory Symptom Coronavirus 2 (SARS-CoV-2) was newly discovered as a beta coronavirus. The virus-induced unexplained etiological pneumonia and is referred to as the 2019 Coronavirus Disease (COVID-19). Though the disease has appeared in a new way, there is no medication for transited patients. So, for diagnosing the COVID-19 infected lungs from X-Ray images, an automated technique has been suggested in this manuscript. In this study, Convolutional neural network (CNN) and VGG19 were used and found accuracy scores of 97% and 67%, respectively. The comparative analysis shows that the proposed method performs better than the solution that exists. Eventually, Precision, Recall, and F1-Score have been extracted and interpreted the model's loss functions in the research. This research has carried out by focusing on essential aspects in terms of COVID-19. Therefore, for the diagnosis of coronavirus infection, the technique can be used effectively.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
从胸部x线图像诊断COVID-19感染肺部
SARS-CoV-2是新发现的一种新型冠状病毒。病毒引起的不明原因的病因性肺炎,被称为2019冠状病毒病(COVID-19)。虽然这种疾病以一种新的方式出现,但没有药物可以治疗过境患者。因此,为了从x射线图像中诊断COVID-19感染的肺部,本文提出了一种自动化技术。在本研究中,使用卷积神经网络(CNN)和VGG19,准确率分别为97%和67%。对比分析表明,该方法的性能优于现有的求解方法。最终,Precision、Recall和F1-Score被提取出来,并在研究中解释了模型的损失函数。这项研究的重点是COVID-19的基本方面。因此,该技术可有效用于冠状病毒感染的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Performance Prediction of A Power Generation Gas Turbine Using An Optimized Artificial Neural Network Model Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach WVEHDD: Weighted Voting based Ensemble System for Heart Disease Detection Predictions of Malaysia Age-Specific Fertility Rates using the Lee-Carter and the Functional Data Approaches Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1