基于x射线图像的双深度神经网络新冠肺炎自动检测方法

M. Gouthami, M. Akhila, P. Kaushika, D. R. Kanth, SRINIVAS KOLLI
{"title":"基于x射线图像的双深度神经网络新冠肺炎自动检测方法","authors":"M. Gouthami, M. Akhila, P. Kaushika, D. R. Kanth, SRINIVAS KOLLI","doi":"10.36346/sarjet.2023.v05i03.003","DOIUrl":null,"url":null,"abstract":"Elderly persons and patients with chronic illnesses are mostly affected by the pandemic disease Covid which also claims lives. It has terrible implications on daily living, public health, and the global economy. Finding positive examples as soon as feasible is difficult. There are only a certain amount of covid test kits available because of the daily rise in cases. Implementing an autonomous detection system as a speedy alternative diagnosis option is essential to stopping the future spread of this epidemic and to treating afflicted persons as soon as possible. Combining radiological imaging with the use of cutting-edge AI tools can be beneficial for the precise diagnosis of this illness and can also help distant areas who lack expert medical care to find a solution. In this project, we have used double deep neural networks to develop an automatic detection of Covid (Convolutional-Neural-Network model, VGG-16 model). Covid chest X-rays will be used as the history data. It will be determined whether the chestX-ray image is covid positive or covid negative.","PeriodicalId":185348,"journal":{"name":"South Asian Research Journal of Engineering and Technology","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Automatic Approach for Detection of COVID-19 using Double Deep Neural Networks with X-ray Images\",\"authors\":\"M. Gouthami, M. Akhila, P. Kaushika, D. R. Kanth, SRINIVAS KOLLI\",\"doi\":\"10.36346/sarjet.2023.v05i03.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Elderly persons and patients with chronic illnesses are mostly affected by the pandemic disease Covid which also claims lives. It has terrible implications on daily living, public health, and the global economy. Finding positive examples as soon as feasible is difficult. There are only a certain amount of covid test kits available because of the daily rise in cases. Implementing an autonomous detection system as a speedy alternative diagnosis option is essential to stopping the future spread of this epidemic and to treating afflicted persons as soon as possible. Combining radiological imaging with the use of cutting-edge AI tools can be beneficial for the precise diagnosis of this illness and can also help distant areas who lack expert medical care to find a solution. In this project, we have used double deep neural networks to develop an automatic detection of Covid (Convolutional-Neural-Network model, VGG-16 model). Covid chest X-rays will be used as the history data. It will be determined whether the chestX-ray image is covid positive or covid negative.\",\"PeriodicalId\":185348,\"journal\":{\"name\":\"South Asian Research Journal of Engineering and Technology\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"South Asian Research Journal of Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36346/sarjet.2023.v05i03.003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"South Asian Research Journal of Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36346/sarjet.2023.v05i03.003","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

老年人和慢性病患者最容易受到大流行疾病Covid的影响,这种疾病也夺去了生命。它对日常生活、公共卫生和全球经济都有可怕的影响。在可行的情况下尽快找到积极的例子是困难的。由于病例每天都在增加,目前只有一定数量的检测试剂盒可用。实施自主检测系统作为一种快速替代诊断选择,对于阻止这一流行病未来的蔓延和尽快治疗患者至关重要。将放射成像与尖端人工智能工具的使用相结合,有利于这种疾病的精确诊断,也可以帮助缺乏专家医疗护理的偏远地区找到解决方案。在本项目中,我们使用双深度神经网络开发了一种自动检测Covid(卷积神经网络模型,VGG-16模型)。新冠胸部x光片作为病史资料。将确定胸片图像是covid阳性还是covid阴性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Automatic Approach for Detection of COVID-19 using Double Deep Neural Networks with X-ray Images
Elderly persons and patients with chronic illnesses are mostly affected by the pandemic disease Covid which also claims lives. It has terrible implications on daily living, public health, and the global economy. Finding positive examples as soon as feasible is difficult. There are only a certain amount of covid test kits available because of the daily rise in cases. Implementing an autonomous detection system as a speedy alternative diagnosis option is essential to stopping the future spread of this epidemic and to treating afflicted persons as soon as possible. Combining radiological imaging with the use of cutting-edge AI tools can be beneficial for the precise diagnosis of this illness and can also help distant areas who lack expert medical care to find a solution. In this project, we have used double deep neural networks to develop an automatic detection of Covid (Convolutional-Neural-Network model, VGG-16 model). Covid chest X-rays will be used as the history data. It will be determined whether the chestX-ray image is covid positive or covid negative.
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