基于肺部x线图像和卷积神经网络的冠状病毒检测新方法

Huda Mubarak Ismail, P. Salehpour, Seyed Hadi Aghdasi Alamdari
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引用次数: 0

摘要

近几个月来,冠状病毒已成为一场全球危机。该病毒扰乱或关闭了许多社会、经济、体育、科学等活动。除了该疾病的医学重要性外,其快速准确的诊断也是一项重要需求。在这项研究中,我们提出了一种基于肺部图像的机器学习和分类算法检测冠状病毒的新方法。一般来说,该方法包括两个步骤。第一步,用一组肺部图像数据集训练卷积神经网络,以确定是否存在病毒感染。第二步,使用另一个网络来检测病毒感染是否被认为是冠状病毒。实验结果表明,诊断正确率为95.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach to Detect Coronavirus Based on Lung x-ray Image and Convolutional Neural Networks
The coronavirus has become a global crisis in recent months. The virus has disrupted or shut down many social, economic, sports, scientific, etc. activities. In addition to the medical importance of this Disease, its rapid and accurate diagnosis is an important need. In this study, we proposed a novel method to detect coronavirus using machine learning and classification algorithms based on lung images. In general, the method consists of two steps. At the first step, a convolutional neural network is trained with a data set of lung images that determine whether a viral infection exists or not. In the second step, another network is used to detect if the existence of viral infection is considered coronavirus or not. Experimental tests have been conducted that show the correct diagnosis can be made with 95.8%.
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