  COVID-19 is an infectious disease that causes respiratory problems similar to
those caused by SARS-CoV (2003). Currently, swab samples are being used for its
diagnosis. The most common testing method used is the RT-PCR method, which has
high specificity but variable sensitivity. AI-based detection has the
capability to overcome this drawback. In this paper, we propose a prospective
method wherein we use chest CT scans to diagnose the patients for COVID-19
pneumonia. We use a set of open-source images, available as individual CT
slices, and full CT scans from a private Indian Hospital to train our model. We
build a 2D segmentation model using the U-Net architecture, which gives the
output by marking out the region of infection. Our model achieves a sensitivity
of 96.428% (95% CI: 88%-100%) and a specificity of 88.39% (95% CI: 82%-94%).
Additionally, we derive a logic for converting our slice-level predictions to
scan-level, which helps us reduce the false positives.
