  In this study, a dataset of X-Ray images from patients with common pneumonia,
Covid-19, and normal incidents was utilized for the automatic detection of the
Coronavirus. The aim of the study is to evaluate the performance of
state-of-the-art Convolutional Neural Network architectures proposed over
recent years for medical image classification. Specifically, the procedure
called transfer learning was adopted. With transfer learning, the detection of
various abnormalities in small medical image datasets is an achievable target,
often yielding remarkable results. The dataset utilized in this experiment is a
collection of 1427 X-Ray images. 224 images with confirmed Covid-19, 700 images
with confirmed common pneumonia, and 504 images of normal conditions are
included. The data was collected from the available X-Ray images on public
medical repositories. With transfer learning, an overall accuracy of 97.82% in
the detection of Covid-19 is achieved.
