The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called CoviLearn) to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, X-rays images can be used to analyze the health of a patient lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 99% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.
翻译:2019年科罗纳病毒(COVID-19)的流行性新科罗纳病毒疾病(COVID-19)在全世界广泛流行,给全球经济造成严重的健康问题和严重影响。对COVID-19的可靠和快速测试一直是研究人员和保健从业人员的一项挑战。在这项工作中,我们在保健网络-物理系统(H-CPS)或智能保健框架(称为CoviLearn)中展示了新型机器学习(ML)综合X光设备,使保健从业人员能够对COVID-19病人进行自动初步筛查。我们提议将X光图像纳入X光设备,用于自动检测COVID-19的合成神经网络模型,用于自动检测CVID的X光谱设备。如果一个人的胸部X光照片呈阳性或负性,则用于检测CVIL的X光,则用于对X光的X光系统进行自动测试。 X光检查中心可以将X光机用于分析。 X光实验室的所有X光机用于对X光的X光检查。