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arXiv · 2312.11561

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

Aryan Tyagi·Aryaman Rao·Shubhanshu Rao·Raj Kumar Singh
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Abstract

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.

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Aryan Tyagi, Aryaman Rao, Shubhanshu Rao, Raj Kumar Singh. 2023-12-17. COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations. https://arxiv.org/abs/2312.11561

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