  Chronic Obstructive Pulmonary Disorder (COPD) is a prevalent respiratory
disease that significantly impacts the quality of life of affected individuals.
This paper presents COPDFlowNet, a novel deep-learning framework that leverages
a custom Generative Adversarial Network (GAN) to generate synthetic
Computational Fluid Dynamics (CFD) velocity flow field images specific to the
trachea of COPD patients. These synthetic images serve as a valuable resource
for data augmentation and model training. Additionally, COPDFlowNet
incorporates a custom Convolutional Neural Network (CNN) architecture to
predict the location of the obstruction site.
