基于深度多层CNN的CXR扫描检测新冠病毒

R. Bhadra, Subhajit Kar
{"title":"基于深度多层CNN的CXR扫描检测新冠病毒","authors":"R. Bhadra, Subhajit Kar","doi":"10.1109/IBSSC51096.2020.9332210","DOIUrl":null,"url":null,"abstract":"Severe Acute Respiratory Syndrome Corona virus 2 (SARS-COV-2) also known as COVID-19 has been emerged as a pandemic throughout the globe recently. Therefore, accurate diagnosis of COVID-19 is necessary to fight against this pandemic situation. In this context, chest X-ray (CXR) scans play an important role in the diagnosis of the corona virus. In this paper, an intelligent detection and classification technique of COVID-19 has been proposed to assist doctors in their diagnostic prediction. A deep multi-layered convolution neural network (CNN) has been proposed to detect COVID-19 accurately from CXR scans. The proposed methodology has experimented on a combination of multiple open source publicly available datasets. Experimental results demonstrate the efficacy of the proposed methodology in COVID-19 detection from CXR images.","PeriodicalId":432093,"journal":{"name":"2020 IEEE Bombay Section Signature Conference (IBSSC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Covid Detection from CXR Scans using Deep Multi-layered CNN\",\"authors\":\"R. Bhadra, Subhajit Kar\",\"doi\":\"10.1109/IBSSC51096.2020.9332210\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Severe Acute Respiratory Syndrome Corona virus 2 (SARS-COV-2) also known as COVID-19 has been emerged as a pandemic throughout the globe recently. Therefore, accurate diagnosis of COVID-19 is necessary to fight against this pandemic situation. In this context, chest X-ray (CXR) scans play an important role in the diagnosis of the corona virus. In this paper, an intelligent detection and classification technique of COVID-19 has been proposed to assist doctors in their diagnostic prediction. A deep multi-layered convolution neural network (CNN) has been proposed to detect COVID-19 accurately from CXR scans. The proposed methodology has experimented on a combination of multiple open source publicly available datasets. Experimental results demonstrate the efficacy of the proposed methodology in COVID-19 detection from CXR images.\",\"PeriodicalId\":432093,\"journal\":{\"name\":\"2020 IEEE Bombay Section Signature Conference (IBSSC)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Bombay Section Signature Conference (IBSSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IBSSC51096.2020.9332210\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Bombay Section Signature Conference (IBSSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IBSSC51096.2020.9332210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

最近,严重急性呼吸系统综合征冠状病毒2 (SARS-COV-2)也被称为COVID-19,在全球范围内出现了大流行。因此,准确诊断COVID-19是抗击疫情的必要条件。在这种情况下,胸部x光扫描在冠状病毒的诊断中发挥着重要作用。本文提出了一种新型冠状病毒智能检测与分类技术,以辅助医生进行诊断预测。提出了一种深度多层卷积神经网络(CNN),用于从CXR扫描中准确检测COVID-19。所提出的方法已经在多个公开可用的开源数据集的组合上进行了实验。实验结果证明了该方法在CXR图像中检测COVID-19的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Covid Detection from CXR Scans using Deep Multi-layered CNN
Severe Acute Respiratory Syndrome Corona virus 2 (SARS-COV-2) also known as COVID-19 has been emerged as a pandemic throughout the globe recently. Therefore, accurate diagnosis of COVID-19 is necessary to fight against this pandemic situation. In this context, chest X-ray (CXR) scans play an important role in the diagnosis of the corona virus. In this paper, an intelligent detection and classification technique of COVID-19 has been proposed to assist doctors in their diagnostic prediction. A deep multi-layered convolution neural network (CNN) has been proposed to detect COVID-19 accurately from CXR scans. The proposed methodology has experimented on a combination of multiple open source publicly available datasets. Experimental results demonstrate the efficacy of the proposed methodology in COVID-19 detection from CXR images.
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
自引率
0.00%
发文量
0
期刊最新文献
Multiclass Spoken Language Identification for Indian Languages using Deep Learning Enhancement of Nighttime Image Visibility Using Wavelet Fusion of Equalized Color Channels and Luminance with Kekre’s LUV Color Space The paradigm shift towards e-Teaching: SWOT analysis from the perspective of Indian teachers Childhood Medulloblastoma Classification Using EfficientNets Unsupervised machine learning in industrial applications: a case study in iron mining
×
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