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

TL;DR

This paper presents a convolutional neural network-based method using U-Net architecture to detect COVID-19 pneumonia from chest CT scans, achieving high sensitivity and specificity, and introduces a logic to improve scan-level predictions.

Contribution

The study introduces a novel AI approach with a U-Net model for COVID-19 detection from CT scans, including a method to convert slice predictions to scan-level results.

Findings

Sensitivity of 96.43% achieved

Specificity of 88.39% achieved

Effective slice-to-scan prediction conversion

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.