COPD Classification in CT Images Using a 3D Convolutional Neural Network

Abstract

Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using diﬀerent imaging modalities is a diﬃcult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.

Citation

@InProceedings{10.1007/978-3-658-29267-6_8,
author={Ahmed, Jalil
and Vesal, Sulaiman
and Durlak, Felix
and Kaergel, Rainer
and Ravikumar, Nishant
and R{\'e}my-Jardin, Martine
and Maier, Andreas",
editor="Tolxdorff, Thomas
and Deserno, Thomas M.
and Handels, Heinz
and Maier, Andreas
and Maier-Hein, Klaus H.
and Palm, Christoph},
title={COPD Classification in CT Images Using a 3D Convolutional Neural Network},
booktitle={Bildverarbeitung für die Medizin 2020},
year={2020},
publisher={Springer Fachmedien Wiesbaden},
address={Wiesbaden},
pages={39--45},
isbn={978-3-658-29267-6}
}