  Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the
impacted patients have been diagnosed with high fever, dry cough, and soar
throat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19
from lung imaging is proved to be a major evidence for early diagnosis of the
disease. Although nucleic acid detection using real-time reverse-transcriptase
polymerase chain reaction (rRT-PCR) remains a gold standard for the detection
of COVID-19, the proposed approach focuses on the automated diagnosis and
prognosis of the disease from a non-contrast chest computed tomography (CT)scan
for timely diagnosis and triage of the patient. The prognosis covers the
quantification and assessment of the disease to help hospitals with the
management and planning of crucial resources, such as medical staff,
ventilators and intensive care units (ICUs) capacity. The approach utilises
deep learning techniques for automated quantification of the severity of
COVID-19 disease via measuring the area of multiple rounded ground-glass
opacities (GGO) and consolidations in the periphery (CP) of the lungs and
accumulating them to form a severity score. The severity of the disease can be
correlated with the medicines prescribed during the triage to assess the
effectiveness of the treatment. The proposed approach shows promising results
where the classification model achieved 93% accuracy on hold-out data.
