# COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
## 1 Introduction  
In recent years, the most talked about topics are coronavirus (in the medical community) and deep learning (in the computer vision and machine learning community). The novel Coronavirus (also known as SARS-CoV-2), belonging to the Corona family and abbreviated as COVID-19, is a virus known to cause lung infections. The virus is highly contagious, as evidenced by the exponential growth of positive cases worldwide in a short period with limited testing. The infection causes severe damage to the lungs, causing pneumonia with accompanying symptoms of a sore throat, dry coughing, sneezing, and high temperature. Moreover, some patients do not show signs, so acting as a carrier is a worrying concern for health organizations. The World Health Organization (WHO) recommended conducting more tests for screening out COVID-19 patients to contain the virus's spread.  
This testing would help in identifying and isolating potential COVID-19 patients from patients of other kinds of infections.  
Generally, X-ray imaging is used for the majority of chest infections, such as pneumonia, bronchitis, and bronchiolitis. Although the use of X-rays is considered non-specific in radiological findings, it will help in further management of a disease. Reverse Transcription Polymerase Chain Reaction (RT-PCR) kits are utilized primarily to test COVID-19 patients. The test kits are expensive and also limited in supply. Moreover, the turnaround time for the test is, on average, 24 h, which considerably slows the testing process. Since the test kits are limited in supply, the use of X-rays can be a viable option, especially in far-flung and rural areas. This pandemic is causing health systems to be overwhelmed with many patients. Chest X-rays could be used in conjunction with related tests to quickly rule out non-COVID patients, ultimately using the burden on health systems. Like any other pneumonia infection, COVID-19 has shown distinct markers on chest X-rays. Sample images for CT scans from the COVIDCT dataset [1] for both COVID and non-COVID cases are illustrated in Figure 1. Representative images for X-rays from the COVIDx dataset [2] for both COVID and non-COVID cases are shown in Figure 2.  
We aim to summarize the most important deep learning networks and then train them on different COVID datasets to predict whether the current classifiers can handle an infection and diagnose it accurately. To this end, we present an experimental survey that reviews and investigates how state-of-the-art deep learning networks can capture fine-grained details from the images and classify them.  
**Our Contributions**: We claim the following contributions in this article:  
1. We present a review of the deep learning networks and summarize the most prominent components of each with focused block diagrams;
2. We employ the deep learning algorithms for COVID-19 datasets to detect infection in the CT and X-ray images;
3. Finally, we provide limitations, challenges, and future directions for the research community.  
Figure 1: Representative images of the COVIDCT [1] dataset employed for training and evaluation of algorithms.