Convolution Neural Networks based COVID-19 Detection using X-ray Images of Human Chest

Lakshmi Sarvani Videla, U. Harita, Nagamani Chippada, Ch. Santhi, A. S. A. L. G. G. Gupta
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引用次数: 3

Abstract

In the recent times, the widespread of COVID-19 around the world has created a pandemic situation which seems to continue without an end. To handle the situation and to deal with this pandemic it is essential that the victims of COVID-19 need to be tested thoroughly so as to start effective treatment. One such test method is to take an x-ray of victim's chest and with the help of technologies like Convolution Neural Networks (CNN), the presence of COVID virus is confirmed at an early stage. With this result effective treatment can be suggested. In this paper an in-depth exploratory analysis is done to find the features that discriminate covid patient x-ray and normal patient x-ray. This paper also attempted to find the accuracy achieved by automatic feature extraction by CNN architecture. The performance of proposed CNN model is compared with the performance of existing VGG16 model.
基于卷积神经网络的人体胸部x射线图像COVID-19检测
最近一段时间,新冠肺炎疫情在全球广泛传播,形成了一种似乎没有尽头的大流行局面。为了应对这种情况和应对这场大流行,必须对COVID-19的受害者进行彻底检测,以便开始有效治疗。其中一种检测方法是对患者的胸部进行x光检查,利用卷积神经网络(CNN)等技术,在早期确认是否存在新冠病毒。根据这一结果,可以提出有效的治疗建议。本文对区分新冠肺炎患者x线与正常患者x线的特征进行了深入的探索性分析。本文还试图找出基于CNN架构的自动特征提取所达到的准确率。将所提CNN模型的性能与现有VGG16模型的性能进行了比较。
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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