COVID-19 detection by X-Ray images and Deep Learning

Abstract

The new Coronavirus, also called COVID-19, appeared in Wuhan, China in December 2019 Spread out big. The virus has now made the COVID-19 disease a worldwide epidemic. This virus has affected the global health, economy, and the daily lives of individuals. The timely diagnosis of COVID-19 is a crucial task as it reduces the risk of pandemic spread. Chest x-rays play an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. Advanced Artificial Intelligence techniques such as Deep Learning have shown high efficiency in detecting patterns such as those that can be found in diseased tissue, so convolution neural networks were evaluated for their ability to detect infected patients from chest X-ray images. The key component of deep learning research is the availability of training data sets. In this study, a parallel Convolutional Neural Networks (CNNs) model has been proposed for the detection of covid-19 infected patients using chest X-ray radiographs. This paper also presents how to evaluate the effectiveness of the state-of-the-art CNN proposed by the scientific community about their expertise in the automatic diagnosis of Covid-19 from thoracic x-rays. To validate results, we trained the CNN model network by using 500 Covid-19 Positive images and 1600 Covid-19 Negative images. It was given a classification accuracy of 90% and validation accuracy of 88%.