  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 meth-ods 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.
