<p>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 pre-trained ImageNet model to extract discriminative features. Subsequently, a shallow feed-forward neural network is trained to classify the available CT scans into three classes namely COVID-19, Community-Acquired Pneumonia, and Normal. Through various ablation studies, we find that a domain-specific pre-processing pipeline improves classification accuracy significantly.  In terms of classification accuracy, our approach when evaluated on publicly available datasets is seen to have an absolute improvement of 6\% F1 score over the baseline model. Further, the estimated infection severity score is observed to be well correlated with radiologists' assessments. The results support the necessity of data-driven pre-processing before implementing learning algorithms.</p>

<p>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 pre-trained ImageNet model to extract discriminative features. Subsequently, a shallow feed-forward neural network is trained to classify the available CT scans into three classes namely COVID-19, Community-Acquired Pneumonia, and Normal. Through various ablation studies, we find that a domain-specific pre-processing pipeline improves classification accuracy significantly.  In terms of classification accuracy, our approach when evaluated on publicly available datasets is seen to have an absolute improvement of 6\% F1 score over the baseline model. Further, the estimated infection severity score is observed to be well correlated with radiologists' assessments. The results support the necessity of data-driven pre-processing before implementing learning algorithms.</p>

<p>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 pre-trained ImageNet model to extract discriminative features. Subsequently, a shallow feed-forward neural network is trained to classify the available CT scans into three classes namely COVID-19, Community-Acquired Pneumonia, and Normal. Through various ablation studies, we find that a domain-specific pre-processing pipeline improves classification accuracy significantly.  In terms of classification accuracy, our approach when evaluated on publicly available datasets is seen to have an absolute improvement of 6\% F1 score over the baseline model. Further, the estimated infection severity score is observed to be well correlated with radiologists' assessments. The results support the necessity of data-driven pre-processing before implementing learning algorithms.</p>