Detecting COVID-19 Related Pneumonia On CT Scans Using Hyperdimensional Computing
Neftali Watkinson, Tony Givargis, Victor Joe, Alexandru Nicolau, Alexander V. Veidenbaum
Abstract
Pneumonia is a common complication associated with COVID-19 infections. Unlike common versions of pneumonia that spread quickly through large lung regions, COVID-19 related pneumonia starts in small localized pockets before spreading over the course of several days. This makes the infection more resilient and with a high probability of developing acute respiratory distress syndrome. Because of the peculiar spread pattern, the use of pulmonary computerized tomography (CT) scans was key in identifying COVID-19 infections. Identifying uncommon pulmonary diseases could be a strong line of defense in early detection of new respiratory infection-causing viruses. In this paper we describe a classification algorithm based on hyperdimensional computing for the detection of COVID-19 pneumonia in CT scans. We test our algorithm using three different datasets. The highest reported accuracy is 95.2% with an F1 score of 0.90, and all three models had a precision of 1 (0 false positives).