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The new coronavirus disease (COVID-19) has been declared a pandemic sinceMarch 2020 by the World Health Organization. It consists of an emerging viralinfection with respiratory tropism that could develop atypical pneumonia.Experts emphasize the importance of early detection of those who have theCOVID-19 virus. In this way, patients will be isolated from other people andthe spread of the virus can be prevented. For this reason, it has become anarea of interest to develop early diagnosis and detection methods to ensure arapid treatment process and prevent the virus from spreading. Since thestandard testing system is time-consuming and not available for everyone,alternative early-screening techniques have become an urgent need. In thisstudy, the approaches used in the detection of COVID-19 based on deep learning(DL) algorithms, which have been popular in recent years, have beencomprehensively discussed. The advantages and disadvantages of differentapproaches used in literature are examined in detail. The Computed Tomographyof the chest and X-ray images give a rich representation of the patient's lungthat is less time-consuming and allows an efficient viral pneumonia detectionusing the DL algorithms. The first step is the pre-processing of these imagesto remove noise. Next, deep features are extracted using multiple types of deepmodels (pre-trained models, generative models, generic neural networks, etc.).Finally, the classification is performed using the obtained features to decidewhether 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 coughanalysis to early screen the COVID-19, and human mobility estimation to limitits spread.

Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review