Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
This addresses the need for more reliable AI-based detection of COVID-19 pneumonia, particularly to complement RT-PCR testing, but it is incremental as it applies an existing method to a specific medical domain.
The paper tackles the problem of diagnosing COVID-19 from chest CT scans by proposing a U-Net-based segmentation model that identifies infected regions, achieving a sensitivity of 96.428% and specificity of 88.39%.
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.