  Coronavirus disease (COVID-19) is an infectious disease caused by a newly
discovered coronavirus. The disease presents with symptoms such as shortness of
breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the
symptoms of the disease increase so much they lead to the death of the
patients. The disease may be asymptomatic in some patients in the early stages,
which can lead to increased transmission of the disease to others. Many studies
have tried to use medical imaging for early diagnosis of COVID-19. This study
attempts to review papers on automatic methods for medical image analysis and
diagnosis of COVID-19. For this purpose, PubMed, Google Scholar, arXiv and
medRxiv were searched to find related studies by the end of April 2020, and the
essential points of the collected studies were summarised. The contribution of
this study is four-fold: 1) to use as a tutorial of the field for both
clinicians and technologists, 2) to comprehensively review the characteristics
of COVID-19 as presented in medical images, 3) to examine automated artificial
intelligence-based approaches for COVID-19 diagnosis based on the accuracy and
the method used, 4) to express the research limitations in this field and the
methods used to overcome them. COVID-19 reveals signs in medical images can be
used for early diagnosis of the disease even in asymptomatic patients. Using
automated machine learning-based methods can diagnose the disease with high
accuracy from medical images and reduce time, cost and error of diagnostic
procedure. It is recommended to collect bulk imaging data from patients in the
shortest possible time to improve the performance of COVID-19 automated
diagnostic methods.
