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

COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations