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

2023/12/17 by Aryan Tyagi, Tyagi, Aryan, Aryaman Rao +5
Engineering · Medicine · #Aerodynamics and Fluid Dynamics Research #Artificial Intelligence (cs.AI) #Chronic Obstructive Pulmonary Disease (COPD) Research #FOS: Computer and information sciences #Lung Cancer Diagnosis and Treatment #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2312.11561

openalex publication_date 2023/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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