  The new coronavirus disease (COVID-19) has been declared a pandemic since
March 2020 by the World Health Organization. It consists of an emerging viral
infection with respiratory tropism that could develop atypical pneumonia.
Experts emphasize the importance of early detection of those who have the
COVID-19 virus. In this way, patients will be isolated from other people and
the spread of the virus can be prevented. For this reason, it has become an
area of interest to develop early diagnosis and detection methods to ensure a
rapid treatment process and prevent the virus from spreading. Since the
standard testing system is time-consuming and not available for everyone,
alternative early-screening techniques have become an urgent need. In this
study, the approaches used in the detection of COVID-19 based on deep learning
(DL) algorithms, which have been popular in recent years, have been
comprehensively discussed. The advantages and disadvantages of different
approaches used in literature are examined in detail. The Computed Tomography
of the chest and X-ray images give a rich representation of the patient's lung
that is less time-consuming and allows an efficient viral pneumonia detection
using the DL algorithms. The first step is the pre-processing of these images
to remove noise. Next, deep features are extracted using multiple types of deep
models (pre-trained models, generative models, generic neural networks, etc.).
Finally, the classification is performed using the obtained features to decide
whether the patient is infected by coronavirus or it is another lung disease.
In this study, we also give a brief review of the latest applications of cough
analysis to early screen the COVID-19, and human mobility estimation to limit
its spread.
