Abstract
Coronavirus (COVID-19) is an ecumenical pandemic that has affected the
whole world drastically by raising a global calamitous situation. Due to
this pernicious disease, millions of people have lost their lives. The
scientists are still far from knowing how to tackle the coronavirus due
to its multiple mutations found around the globe. Standard testing
technique called Polymerase Chain Reaction (PCR) for the clinical
diagnosis of COVID-19 is expensive and time consuming. However, to
assist specialists and radiologists in COVID-19 detection and diagnosis,
deep learning plays an important role. Many research efforts have been
done that leverage deep learning techniques and technologies for the
identification or categorization of COVID-19 positive patients, and
these techniques are proved to be a powerful tool that can automatically
detect or diagnose COVID-19 cases. In this paper, we identify
significant challenges regarding deep learning-based systems and
techniques that use different medical imaging modalities, including
Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat
COVID-19 outbreak. We also pinpoint important research questions for
each category of challenges. The challenges highlighted in this paper
will call an attention to the noticeable weaknesses and problems in the
existing deep learning based COVID-19 detection systems and techniques.
Moreover, the research questions for each challenge will guide the
researchers to come up with novel solutions in COVID-19 detection.