Abstract The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of
China in December 2019, spread rapidly around the world and became a pandemic. It has
caused a devastating effect on both daily lives, public health, and the global economy. It is
critical to detect the positive cases as early as possible so as to prevent the further spread of
this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools
has increased as there are no accurate automated toolkits available. Recent findings …

Many health systems over the world have collapsed due to limited capacity and a dramatic
increase of suspected COVID-19 cases. What has emerged is the need for finding an
efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to
diagnose the suspected cases. In this paper, we propose a deep learning architecture to
detect Covid-19 Coronavirus in chest radiographs. This architecture contains one network to
classify images as either normal or Covid-19 Coronavirus. In this paper, we adopt ResNet …