COVID-VR: A Deep Learning COVID-19 Classification Model Using Volume-Rendered Computer Tomography
This addresses the challenge of automating COVID-19 and pulmonary disease diagnosis for healthcare systems, but it is incremental as it builds on existing deep learning approaches with a new input type.
The paper tackled the problem of classifying pulmonary diseases like COVID-19 from CT scans by introducing COVID-VR, a deep learning model that uses volume-rendered images from multiple angles for a comprehensive lung view, and it performed competitively against slice-based methods on private and public datasets.
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.