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COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, Chinaand spread across the globe with unprecedented effect and has now become thegreatest crisis of the modern era. The COVID-19 has proved much more pervasivedemands for diagnosis that has driven researchers to develop more intelligent,highly responsive and efficient detection methods. In this work, we focus onproposing AI tools that can be used by radiologists or healthcare professionalsto diagnose COVID-19 cases in a quick and accurate manner. However, the lack ofa publicly available dataset of X-ray and CT images makes the design of such AItools a challenging task. To this end, this study aims to build a comprehensivedataset of X-rays and CT scan images from multiple sources as well as providesa simple but an effective COVID-19 detection technique using deep learning andtransfer learning algorithms. In this vein, a simple convolution neural network(CNN) and modified pre-trained AlexNet model are applied on the prepared X-raysand CT scan images dataset. The result of the experiments shows that theutilized models can provide accuracy up to 98 % via pre-trained network and94.1 % accuracy by using the modified CNN.

Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms