COVID-VR: A Deep Learning COVID-19 Classification Model Using
  Volume-Rendered Computer Tomography
Noemi Maritza L. Romero Ricco Vasconcellos Mariana R. Mendoza João L. D. Comba
The COVID-19 pandemic presented numerous challenges to healthcare systems
worldwide. Given that lung infections are prevalent among COVID-19 patients,
chest Computer Tomography (CT) scans have frequently been utilized as an
alternative method for identifying COVID-19 conditions and various other types
of pulmonary diseases. Deep learning architectures have emerged to automate the
identification of pulmonary disease types by leveraging CT scan slices as
inputs for classification models. This paper introduces COVID-VR, a novel
approach for classifying pulmonary diseases based on volume rendering images of
the lungs captured from multiple angles, thereby providing a comprehensive view
of the entire lung in each image. To assess the effectiveness of our proposal,
we compared it against competing strategies utilizing both private data
obtained from partner hospitals and a publicly available dataset. The results
demonstrate that our approach effectively identifies pulmonary lesions and
performs competitively when compared to slice-based methods.