Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2006.13212v1
• Date: Tue, 23 Jun 2020 06:50:41 GMT
• Title: Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
• Authors: Rohit Lokwani, Ashrika Gaikwad, Viraj Kulkarni, Aniruddha Pant, Amit Kharat
• Abstract summary: COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV 2003. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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