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COVID-VR: A Deep Learning COVID-19 Classification Model Using Volume-Rendered Computer Tomography

arXiv 2308.01433 ↗First posted 2 Aug 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 2 Aug 2023.

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References · 14

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2 more
  1. 2021COVID-view: Diagnosis of COVID-19 using Chest CTcited by 2
  2. 2020Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patientscited by 1

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