基于云的医疗物联网电子健康服务——基于深度卷积神经网络的脑肿瘤检测模型

Q2 Social Sciences Electronic Government Pub Date : 2020-02-22 DOI:10.1504/eg.2020.10023759
M. Ganesan, N. Sivakumar, M. Thirumaran
{"title":"基于云的医疗物联网电子健康服务——基于深度卷积神经网络的脑肿瘤检测模型","authors":"M. Ganesan, N. Sivakumar, M. Thirumaran","doi":"10.1504/eg.2020.10023759","DOIUrl":null,"url":null,"abstract":"In the present days, e-health services offer various decision support systems in healthcare sector. These systems make use of internet of medical things (IoMT) devices and cloud platform to offer services to millions of people. In this paper, we develop an IoT with cloud-based brain tumour detection model using convolution neural network (CNN). Here, the input MRI brain images are captured by the use of medical equipments as well as IoT devices is used to transmit data to the cloud. In the cloud, the D-CNN model can be executed to identify the presence of disease and classify the brain tumour as malignant or benign. The presented D-CNN model is employed to a set of benchmark BRATS 2015 challenge dataset. The presented model attains maximum classifier performance with the sensitivity value of 97.17, specificity of 98.77 and accuracy of 98.07.","PeriodicalId":35551,"journal":{"name":"Electronic Government","volume":"16 1","pages":"69-83"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Internet of Medical Things with Cloud based e-Health Services for Brain Tumor Detection Model using Deep Convolution Neural Network\",\"authors\":\"M. Ganesan, N. Sivakumar, M. Thirumaran\",\"doi\":\"10.1504/eg.2020.10023759\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the present days, e-health services offer various decision support systems in healthcare sector. These systems make use of internet of medical things (IoMT) devices and cloud platform to offer services to millions of people. In this paper, we develop an IoT with cloud-based brain tumour detection model using convolution neural network (CNN). Here, the input MRI brain images are captured by the use of medical equipments as well as IoT devices is used to transmit data to the cloud. In the cloud, the D-CNN model can be executed to identify the presence of disease and classify the brain tumour as malignant or benign. The presented D-CNN model is employed to a set of benchmark BRATS 2015 challenge dataset. The presented model attains maximum classifier performance with the sensitivity value of 97.17, specificity of 98.77 and accuracy of 98.07.\",\"PeriodicalId\":35551,\"journal\":{\"name\":\"Electronic Government\",\"volume\":\"16 1\",\"pages\":\"69-83\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronic Government\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/eg.2020.10023759\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Government","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/eg.2020.10023759","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0

摘要

目前,电子医疗服务为医疗保健部门提供了各种决策支持系统。这些系统利用医疗物联网(IoMT)设备和云平台为数百万人提供服务。在本文中,我们利用卷积神经网络(CNN)开发了一个基于云的物联网脑肿瘤检测模型。在这里,使用医疗设备捕获输入的MRI大脑图像,并使用物联网设备将数据传输到云端。在云中,可以执行D-CNN模型来识别疾病的存在,并将脑肿瘤分类为恶性或良性。将所提出的D-CNN模型应用于一组基准BRATS 2015挑战数据集。该模型灵敏度为97.17,特异度为98.77,准确率为98.07,分类器性能最佳。
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
Internet of Medical Things with Cloud based e-Health Services for Brain Tumor Detection Model using Deep Convolution Neural Network
In the present days, e-health services offer various decision support systems in healthcare sector. These systems make use of internet of medical things (IoMT) devices and cloud platform to offer services to millions of people. In this paper, we develop an IoT with cloud-based brain tumour detection model using convolution neural network (CNN). Here, the input MRI brain images are captured by the use of medical equipments as well as IoT devices is used to transmit data to the cloud. In the cloud, the D-CNN model can be executed to identify the presence of disease and classify the brain tumour as malignant or benign. The presented D-CNN model is employed to a set of benchmark BRATS 2015 challenge dataset. The presented model attains maximum classifier performance with the sensitivity value of 97.17, specificity of 98.77 and accuracy of 98.07.
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
Electronic Government
Electronic Government Social Sciences-Public Administration
CiteScore
2.30
自引率
0.00%
发文量
48
期刊介绍: Electronic Government, a fully refereed journal, publishes articles that present current practice and research in the area of e-government.
期刊最新文献
Readiness and acceptability for use of e-government services in Kuwait: a case study Why does effort expectancy not have a significant effect on the utilisation of e-reports in Sleman Regency? Social Media Use for Public Policy Making Cycle A Meta-Analysis Critical path-dependencies affecting digital government innovation in low-income countries: a case study from Woredas in Ethiopia The Effects of Mobile Network Performance and Affordability on E-Government Development.
×
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