The Covid-19 Detection with Contactless Method based on Deep Learning

Ismail, Fibriyanti, Zas Ressy Aidha, Menhendry, K. Hawari
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引用次数: 1

Abstract

The spread of corona virus diseases among medical workers is a big problem in this pandemic corona virus. The are many medical workers suffer and some of them died. The infected medical workers of corona virus are caused through directed or closed contacts between infected patients and medical workers. The closed and directed contact in medical services take place especially in diagnostic process. In order to handle this problem, the proposed method prevents the closed or directed contacts of medical workers to the suspected patients. The method uses RMI images or chest X-ray images data to predict the infected suspects. The proposed method is detecting the infected lung X-Ray image through Deep Learning model. It uses pre-trained model of GoogleNet, with modification. The Confusion Matrix and ROC curve are used to measure accuracy the predicted. They show that proposed method has high accuracy. Finally, the proposed method is able to replace closed or directed contact diagnostic done by medical workers to Covid-19 suspects. It has ability to handle the spread of Covid-19 among medical workers.
基于深度学习的新型冠状病毒非接触检测方法
冠状病毒疾病在医务工作者中的传播是本次冠状病毒大流行的一个大问题。许多医务工作者遭受痛苦,其中一些人死亡。冠状病毒感染的医务人员是通过受感染患者与医务人员直接接触或密切接触引起的。在医疗服务中,特别是在诊断过程中,发生了密切和直接的接触。为了解决这一问题,该方法防止了医务工作者与疑似患者的密切或直接接触。该方法使用RMI图像或胸部x线图像数据来预测感染嫌疑人。提出的方法是通过深度学习模型检测感染的肺部x射线图像。它使用GoogleNet预训练模型,经过修改。使用混淆矩阵和ROC曲线来衡量预测的准确性。结果表明,该方法具有较高的精度。最后,该方法能够取代医务工作者对Covid-19嫌疑人进行的封闭或直接接触诊断。它有能力应对Covid-19在医务工作者中的传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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