{"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}
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.