  Coronavirus, or COVID-19, is a hazardous disease that has endangered the
health of many people around the world by directly affecting the lungs.
COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also
has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and
computed tomography (CT) imaging modalities are widely used to obtain a fast
and accurate medical diagnosis. Identifying COVID-19 from these medical images
is extremely challenging as it is time-consuming and prone to human errors.
Hence, artificial intelligence (AI) methodologies can be used to obtain
consistent high performance. Among the AI methods, deep learning (DL) networks
have gained popularity recently compared to conventional machine learning (ML).
Unlike ML, all stages of feature extraction, feature selection, and
classification are accomplished automatically in DL models. In this paper, a
complete survey of studies on the application of DL techniques for COVID-19
diagnostic and segmentation of lungs is discussed, concentrating on works that
used X-Ray and CT images. Additionally, a review of papers on the forecasting
of coronavirus prevalence in different parts of the world with DL is presented.
Lastly, the challenges faced in the detection of COVID-19 using DL techniques
and directions for future research are discussed.
