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

TL;DR

COVID-VR introduces a deep learning model that classifies pulmonary diseases from volume-rendered lung images, offering a comprehensive view that improves COVID-19 detection compared to traditional slice-based methods.

Contribution

The paper presents a novel volume rendering approach for lung imaging combined with deep learning, enhancing COVID-19 classification accuracy over existing slice-based techniques.

Findings

Effective identification of pulmonary lesions

Competitive performance on private and public datasets

Improved lung disease classification accuracy

Abstract

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.