A Deep Learning Framework for Pulmonary Disease Classification Using Volume-Rendered CTs - Romero, Noemi Maritza L.; Soares, Ricco V. C.; Recamonde-Mendoza, Mariana; Comba, João L. D. (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
Accurate classification of pulmonary diseases is critical for clinical decision-making, and deep learning models using chest CT scans have become a key tool in this task. Most existing approaches rely on 2D CT slices, which provide limited views and may miss important spatial patterns across the lung volume. To address this, we introduce CT-VR, a novel classification approach that leverages 3D volume-rendered images captured from multiple angles. By incorporating multi-view volume rendering, CT-VR enhances the model ability to detect and differentiate between pulmonary conditions. We evaluate the method using COVID-19 datasets as a primary case study, which include private datasets from partner hospitals and a publicly available benchmark. Results demonstrate that our approach improves lesion identification and delivers performance compared to traditional slice-based models, highlighting its potential as a more effective solution for lung disease classification.