COVID-19 Detection Model on Chest CT Scan and X-ray Images Using VGG16 Convolutional Neural Network

Shannen Latisha, Albert Christopher Halim, Regan Ricardo, Derwin Suhartono
{"title":"COVID-19 Detection Model on Chest CT Scan and X-ray Images Using VGG16 Convolutional Neural Network","authors":"Shannen Latisha, Albert Christopher Halim, Regan Ricardo, Derwin Suhartono","doi":"10.1109/ISRITI54043.2021.9702839","DOIUrl":null,"url":null,"abstract":"In this pandemic of COVID-19, many people's lives are highly affected in various kinds of aspects. Tests are conducted due to the rising number of infected people, with the PCR test as the current gold standard for many. However, many experts consider the PCR test inaccurate due to the resulting false negative and false positive test results. In order to solve the problem, through this paper, the use of a deep learning model is proposed based on a customized VGG16 CNN as a way to identify the presence COVID-19 virus. The biomarkers used in this paper are X-ray and CT scan images of the lungs. At the end of the research, it can be concluded that both CT scan and X-ray images can be used to detect COVID-19 by using VGG16. However, by comparing the performance of the proposed X-ray and CT scan biomarker-based models, it can be inferred that the X-ray biomarker-based model obtained a higher accuracy score of 97% compared to the CT scan-based model with 93% accuracy. This research proved that the X-ray model got a better score and is a better alternative than CT scan, although both have potential and can be considered accurate alternatives to the PCR tests.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRITI54043.2021.9702839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this pandemic of COVID-19, many people's lives are highly affected in various kinds of aspects. Tests are conducted due to the rising number of infected people, with the PCR test as the current gold standard for many. However, many experts consider the PCR test inaccurate due to the resulting false negative and false positive test results. In order to solve the problem, through this paper, the use of a deep learning model is proposed based on a customized VGG16 CNN as a way to identify the presence COVID-19 virus. The biomarkers used in this paper are X-ray and CT scan images of the lungs. At the end of the research, it can be concluded that both CT scan and X-ray images can be used to detect COVID-19 by using VGG16. However, by comparing the performance of the proposed X-ray and CT scan biomarker-based models, it can be inferred that the X-ray biomarker-based model obtained a higher accuracy score of 97% compared to the CT scan-based model with 93% accuracy. This research proved that the X-ray model got a better score and is a better alternative than CT scan, although both have potential and can be considered accurate alternatives to the PCR tests.
基于VGG16卷积神经网络的胸部CT扫描和x线图像COVID-19检测模型
在这场新冠肺炎大流行中,许多人的生活在各个方面受到严重影响。由于感染人数不断增加,因此进行了检测,对许多人来说,PCR检测是目前的黄金标准。然而,许多专家认为PCR检测不准确,因为会产生假阴性和假阳性的检测结果。为了解决这一问题,通过本文提出了一种基于定制VGG16 CNN的深度学习模型作为识别存在的COVID-19病毒的方法。本文使用的生物标志物是肺部的x射线和CT扫描图像。在研究的最后,可以得出结论,CT扫描和x射线图像都可以使用VGG16检测COVID-19。然而,通过比较所提出的基于x射线和CT扫描生物标志物的模型的性能,可以推断,基于x射线生物标志物的模型获得了97%的准确率,而基于CT扫描的模型准确率为93%。本研究证明,x射线模型获得了更好的评分,是比CT扫描更好的替代方法,尽管两者都有潜力,可以被认为是PCR检测的准确替代品。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信