COVID-19 Detection Using Chest X-Ray Images Based on Deep Learning

Sudeshna Sani, Abhijit Bera, D. Mitra, Kalyani Maity Das
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Abstract

Global public health will be severely impacted by the successive waves of emerging COVID-19 disease. Since 2019 people get sick and die in our daily lives placing a massive burden on our health system. One of the crucial factors that has led to the virus's fast spread is a protracted clinical testing gap before discovering of a positive or negative result. A detection system based on deep learning was developed by using chest X-ray(CXR) images of Covid19 patient and healthy people. In this regard the Convolution Neural Network along with other DNNs have been proved to produce good results. To improve the COVID-19 detection accuracy, we developed model using the deep learning(CNN) approach where we observed an accuracy of 96%. We validated the accuracy by using same dataset through a pretrained VGG16 model and an LSTM model which produced excellent reliable results. Our aim of this research is to implement a reliable Deep Learning model to detect presence of Covid-19 in case of limited availability of chest-Xray images.
基于深度学习的胸部x线图像COVID-19检测
新出现的COVID-19疾病的连续浪潮将严重影响全球公共卫生。自2019年以来,人们在日常生活中生病和死亡,给我们的卫生系统带来了巨大的负担。导致病毒快速传播的关键因素之一是,在发现阳性或阴性结果之前,临床检测的时间间隔很长。利用新冠肺炎患者和健康人的胸部x光片(CXR)图像,开发了基于深度学习的检测系统。在这方面,卷积神经网络和其他深度神经网络已经被证明可以产生良好的效果。为了提高COVID-19检测的准确性,我们使用深度学习(CNN)方法开发了模型,我们观察到准确率为96%。我们使用相同的数据集,通过预训练的VGG16模型和LSTM模型验证了准确性,得到了非常好的可靠结果。我们这项研究的目的是实现一个可靠的深度学习模型,在胸部x光图像可用性有限的情况下检测Covid-19的存在。
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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