CNN与CNN- lstm联合胸片检测COVID-19的比较

IF 1.4 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Julio Fachrel, Anindya Apriliyanti Pravitasari, I. Yulita, Mulya Nurmansyah Ardhisasmita, F. Indrayatna
{"title":"CNN与CNN- lstm联合胸片检测COVID-19的比较","authors":"Julio Fachrel, Anindya Apriliyanti Pravitasari, I. Yulita, Mulya Nurmansyah Ardhisasmita, F. Indrayatna","doi":"10.5267/j.dsl.2023.2.004","DOIUrl":null,"url":null,"abstract":"COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"262 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A comparison between CNN and combined CNN-LSTM for chest X-ray based COVID-19 detection\",\"authors\":\"Julio Fachrel, Anindya Apriliyanti Pravitasari, I. Yulita, Mulya Nurmansyah Ardhisasmita, F. Indrayatna\",\"doi\":\"10.5267/j.dsl.2023.2.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.\",\"PeriodicalId\":38141,\"journal\":{\"name\":\"Decision Science Letters\",\"volume\":\"262 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Science Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5267/j.dsl.2023.2.004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Science Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5267/j.dsl.2023.2.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 1

摘要

通过放射检查检测COVID-19受到青睐,因为它比实验室方法快速且产生更准确的结果。然而,当它感染了许多人并给医疗系统带来压力时,在患者中快速、自动检测COVID-19的需求变得至关重要。本研究提出用机器学习方法从胸部x线(CXR)图像中检测COVID-19。本文的主要贡献是比较了两种强大的深度学习模型,即卷积神经网络(CNN)和CNN与长短期记忆(LSTM)的结合。在组合模型中,推荐使用CNN进行特征提取,使用LSTM的特征对COVID-19进行分类。本研究使用的数据集为4095张CXR图像,其中包括1400张正常图像,1350张COVID-19图像和1345张肺炎图像。CNN和CNN- lstm都在类似的实验设置中执行,并使用混淆矩阵进行评估。实验结果证明,CNN- ltsm优于CNN深度学习模型,总体准确率约为98.78%。此外,它的准确率和召回率分别为99%和98%。这些发现将有助于快速准确地检测COVID-19。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison between CNN and combined CNN-LSTM for chest X-ray based COVID-19 detection
COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Decision Science Letters
Decision Science Letters Decision Sciences-Decision Sciences (all)
CiteScore
3.40
自引率
5.30%
发文量
49
审稿时长
20 weeks
×
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