Covid-19 disease recognition using distributed data mining and deep learning

AHMAD ABADLEH; AFNAN AL-SARAIREH; HAMZEH EYAL SALMAN; ANAS AL- AKASASBEH; SAQER ALJA'AFREH; AWNI HAMMOURI; RA'FAT AL-MSIE'DEEN; AHMAD HASSANAT

Since the first COVID-19 case was reported at the end of 2019 in China, the COVID-19 virus moved to almost every country with rapidly spread among people. It has a destructive effect on people's health and their daily life. The world health organization (WHO) in April 2020 officially declared the COVID-19 as a pandemic. To date, there is no specific treatment for COVID-19. Therefore, the detection of COVID-19 disease is required to avoid the fast spread of disease and halt its chain. In this article, we suggest an approach to recognize COVID-19 through X-ray images using distributed data mining techniques and Convolutional Neural Networks. To validate the proposed approach, we apply it to a public dataset consisting of 2,905 chest X-ray images for COVID-19 patients in addition to Viral Pneumonia and Normal images. The results show that the suggested approach gives promising results in terms of well-known evaluation metrics in the subject.