Automatically Detect the coronavirus (COVID-19) disease using Chest X-ray and CT images

Krishna Mridha, Smit Kumbhani, A. Pandey, P. Damodharan
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引用次数: 1

Abstract

Nowadays, Covid -19 is one of the major problems in the world. It is spread very quickly by connecting or touching with a covid positive person. To detect the covid - 19, we have to use the testing kits. But we don't have that many kits for testing the covid-19 because the affected number of people is increasing day by day. To solve these big issues, we are introducing one another method. To detect the covid-19 we need to use either chest X-ray's image or Computed Tomography (CT) images. The reason behind to implement of the model is very simple and easy because almost every hospital diagnostic center has X-rays imaging facilities. To identify the covid positive or negative cases, we do not require any kits. In this article, we are introducing one novel model, the process of building the model, and the dataset that we have used to train our model. To train the model we have used almost 1000 chest X-ray images and 700 CT images. For training the model, we are using deep learning algorithms like VGG16, VGG19, Inception V3, RestnetSO, and Xception. We also compare all of the algorithms with some comparison graphs. Among all of the deep learning models, the Inception V3 performs the best accuracy in both datasets.
使用胸部x光和CT图像自动检测冠状病毒(COVID-19)疾病
当前,新冠肺炎疫情是世界面临的重大问题之一。它通过与covid阳性患者接触或接触而迅速传播。为了检测covid - 19,我们必须使用检测试剂盒。但我们没有那么多的试剂盒来检测covid-19,因为受影响的人数每天都在增加。为了解决这些大问题,我们正在引入另一种方法。为了检测covid-19,我们需要使用胸部x射线图像或计算机断层扫描(CT)图像。实施该模型的原因非常简单,因为几乎每家医院的诊断中心都有x射线成像设备。为了识别covid阳性或阴性病例,我们不需要任何试剂盒。在本文中,我们将介绍一个新的模型,构建模型的过程,以及我们用来训练模型的数据集。为了训练模型,我们使用了近1000张胸部x射线图像和700张CT图像。为了训练模型,我们使用了VGG16、VGG19、Inception V3、RestnetSO和Xception等深度学习算法。我们还用一些比较图对所有算法进行了比较。在所有的深度学习模型中,Inception V3在两个数据集中都表现出最好的准确性。
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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