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首页> 外文期刊>Journal of Experimental & Theoretical Artificial Intelligence >Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing
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Detection of COVID-19 Disease in Chest X-Ray Images with capsul networks: application with cloud computing

机译：用CAPSUL网络检测胸部X射线图像中的Covid-19疾病：云计算应用

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摘要

Today, health is the most important value of human life pandemics at different time intervals in the world history. Finally, the COVID-19 outbreak that occurred in Wuhan, China in December 2019, spread to the whole world in a really short time and caused a pandemic. In order to prevent this pandemic, early detection of the COVID-19 is very important. In this study, chest x-ray images of 1019 patients with open-source dataset were taken from four different sources. The images were analysed using Capsule Networks (CapsNet) model, which is one of the deep learning methods, whose popularity has increased in recent years. With the designed CapsNet model, individuals with COVID-19 disease were tried to be identified. The designed CapsNet model can detect COVID-19 disease with an accuracy rate of 98.02%. The obtained model cloud computing application was developed in order to use the work performed faster and easier.
机译：今天，健康是世界历史上不同时间间隔的人类生命流行病最重要的价值。 最后，在2019年12月，中国武汉发生的Covid-19爆发，在一段时间内蔓延到全世界，造成了大流行。 为了防止这种大流行，早期检测Covid-19非常重要。 在这项研究中，来自四种不同来源的1019名开源数据集患者的胸X射线图像。 使用胶囊网络（CAPSNET）模型进行分析图像，该模型是近年来的深入学习方法之一。 通过设计的帽模型，试图确定具有Covid-19疾病的个体。 设计的载玻片模型可以检测Covid-19疾病，精度率为98.02％。 所获得的模型云计算应用程序是开发的，以便使用工作更快更容易。

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