The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to have
a significant impact on the global population. To effectively triage
patients and understand the progression of the disease, a metric-based
analysis of diagnostic techniques is necessary. The objective of the
present study is to identify COVID-19 from chest CT scans and determine
the extent of severity, defined by a severity score that indicates the
volume of infection. An unsupervised preprocessing pipeline is proposed
to extract relevant clinical features and utilize this information to
employ a pretrained ImageNet EfficientNetB5 model to extract
discriminative features. Subsequently, a shallow feed-forward neural
network is trained to classify the CT scans into three classes, namely
COVID-19, Community-Acquired Pneumonia, and Normal. Through various
ablation studies, we find that a domain-specific preprocessing pipeline
has a significant positive impact on classification accuracy. The
infection segmentation mask generated from the preprocessed pipeline
performs better than state-of-the-art supervised semantic segmentation
models. Further, the estimated infection severity score is observed to
be well correlated with radiologists' assessments. The results confirm
the importance of domain-specific preprocessing for training machine
learning algorithms.