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

arXiv: https://arxiv.org/abs/2312.11561 · HTML: https://mlsanity.com/paper/2312.11561/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Abstract (arXiv)

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.