COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations (Student Abstract)

Authors

  • Aryan Tyagi Delhi Technological University
  • Aryaman Rao Delhi Technological University
  • Shubhanshu Rao Delhi Technological University
  • Raj Kumar Singh Delhi Technological University

DOI:

https://doi.org/10.1609/aaai.v38i21.30520

Keywords:

Computer Vision, Machine Learning, Applications Of AI

Abstract

Chronic Obstructive Pulmonary Disorder (COPD) is a prevalent respiratory disease that significantly impacts the quality of life of affected individuals. This paper presents COPD-FlowNet, 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, COPD-FlowNet incorporates a custom Convolutional Neural Network (CNN) architecture to predict the location of the obstruction site.

Published

2024-03-24

How to Cite

Tyagi, A., Rao, A., Rao, S., & Singh, R. K. (2024). COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23671-23672. https://doi.org/10.1609/aaai.v38i21.30520