  COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China
and spread across the globe with unprecedented effect and has now become the
greatest crisis of the modern era. The COVID-19 has proved much more pervasive
demands for diagnosis that has driven researchers to develop more intelligent,
highly responsive and efficient detection methods. In this work, we focus on
proposing AI tools that can be used by radiologists or healthcare professionals
to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of
a publicly available dataset of X-ray and CT images makes the design of such AI
tools a challenging task. To this end, this study aims to build a comprehensive
dataset of X-rays and CT scan images from multiple sources as well as provides
a simple but an effective COVID-19 detection technique using deep learning and
transfer learning algorithms. In this vein, a simple convolution neural network
(CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays
and CT scan images dataset. The result of the experiments shows that the
utilized models can provide accuracy up to 98 % via pre-trained network and
94.1 % accuracy by using the modified CNN.
