Detecting COVID-19 Related Pneumonia on CT Scans using Hyperdimensional Computing

  1. Neftali Watkinson
  2. Tony Givargis
  3. Victor Joe
  4. Alexandru Nicolau
  5. Alexander Veidenbaum

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Abstract

Pneumonia is a common complication associated with COVID-19 infections. Unlike common versions of pneumonia 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).

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    SciScore for 10.1101/2021.05.21.21257631: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2021.05.21.21257631v1 on medRxiv