医学分析中基于深度表征的COVID-19图像分类技术

Morarjee Kolla, H. R. Rao, N. Kumar
{"title":"医学分析中基于深度表征的COVID-19图像分类技术","authors":"Morarjee Kolla, H. R. Rao, N. Kumar","doi":"10.1063/5.0057943","DOIUrl":null,"url":null,"abstract":"Covid-19 is a fast-growing disease that affects human health with contacts nowadays. The medical community has not found any vaccine for immediate use, and some countries recently released vaccines. The human health and financial status of various countries spoiled recently with this virus. COVID-19 vaccine research is at the clinical trial stage in many countries. Mainly this disease affects the lungs of the patients. Recently deep learning approaches are widely using in radiographic image classifications with large-scale data. Convolutional Neural Networks (CNN) are widely used to diagnose COVID-19 pneumonia classification on Chest radiographic images to help radiologists in medical analysis. Recently some researchers developed tools to detect the virus, and they reduce the time of chest X-ray interpretation. This article discusses methods that can help protect themselves from those already infected with the virus by classifying the large-scale radiographic images with deep learning models. This study compares various methodologies and observes exciting insights for future research directions. © 2021 Author(s).","PeriodicalId":21797,"journal":{"name":"SEVENTH INTERNATIONAL SYMPOSIUM ON NEGATIVE IONS, BEAMS AND SOURCES (NIBS 2020)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COVID-19 image classification techniques in medical analysis using deep representations\",\"authors\":\"Morarjee Kolla, H. R. Rao, N. Kumar\",\"doi\":\"10.1063/5.0057943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Covid-19 is a fast-growing disease that affects human health with contacts nowadays. The medical community has not found any vaccine for immediate use, and some countries recently released vaccines. The human health and financial status of various countries spoiled recently with this virus. COVID-19 vaccine research is at the clinical trial stage in many countries. Mainly this disease affects the lungs of the patients. Recently deep learning approaches are widely using in radiographic image classifications with large-scale data. Convolutional Neural Networks (CNN) are widely used to diagnose COVID-19 pneumonia classification on Chest radiographic images to help radiologists in medical analysis. Recently some researchers developed tools to detect the virus, and they reduce the time of chest X-ray interpretation. This article discusses methods that can help protect themselves from those already infected with the virus by classifying the large-scale radiographic images with deep learning models. This study compares various methodologies and observes exciting insights for future research directions. © 2021 Author(s).\",\"PeriodicalId\":21797,\"journal\":{\"name\":\"SEVENTH INTERNATIONAL SYMPOSIUM ON NEGATIVE IONS, BEAMS AND SOURCES (NIBS 2020)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SEVENTH INTERNATIONAL SYMPOSIUM ON NEGATIVE IONS, BEAMS AND SOURCES (NIBS 2020)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0057943\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SEVENTH INTERNATIONAL SYMPOSIUM ON NEGATIVE IONS, BEAMS AND SOURCES (NIBS 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/5.0057943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

Covid-19是当今一种快速发展的疾病,通过接触影响人类健康。医学界还没有找到任何可以立即使用的疫苗,一些国家最近发布了疫苗。最近,这种病毒破坏了各国的人类健康和财政状况。COVID-19疫苗研究在许多国家处于临床试验阶段。这种疾病主要影响患者的肺部。近年来,深度学习方法被广泛应用于具有大规模数据的放射图像分类中。卷积神经网络(CNN)被广泛用于胸片图像上的COVID-19肺炎分类诊断,以帮助放射科医生进行医学分析。最近,一些研究人员开发了检测这种病毒的工具,减少了胸部x光检查的时间。本文讨论了通过深度学习模型对大规模放射图像进行分类,可以帮助保护自己免受已经感染病毒的人的方法。本研究比较了各种研究方法,并对未来的研究方向提出了令人振奋的见解。©2021作者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COVID-19 image classification techniques in medical analysis using deep representations
Covid-19 is a fast-growing disease that affects human health with contacts nowadays. The medical community has not found any vaccine for immediate use, and some countries recently released vaccines. The human health and financial status of various countries spoiled recently with this virus. COVID-19 vaccine research is at the clinical trial stage in many countries. Mainly this disease affects the lungs of the patients. Recently deep learning approaches are widely using in radiographic image classifications with large-scale data. Convolutional Neural Networks (CNN) are widely used to diagnose COVID-19 pneumonia classification on Chest radiographic images to help radiologists in medical analysis. Recently some researchers developed tools to detect the virus, and they reduce the time of chest X-ray interpretation. This article discusses methods that can help protect themselves from those already infected with the virus by classifying the large-scale radiographic images with deep learning models. This study compares various methodologies and observes exciting insights for future research directions. © 2021 Author(s).
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
×
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