ML Anthology

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

Aryan Tyagi, Aryaman Rao, Shubhanshu Rao, Raj Kumar Singh
AAAI 2024 pp. 23671-23672
doi:10.1609/AAAI.V38I21.30520 /aaai/2024/tyagi2024aaai-copd/

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.

PDF AAAI Semantic Scholar

Cite

Text

Tyagi et al. "COPD-FlowNet: Elevating Non-Invasive COPD Diagnosis with CFD Simulations (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30520

Markdown

[Tyagi et al. "COPD-FlowNet: Elevating Non-Invasive COPD Diagnosis with CFD Simulations (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/tyagi2024aaai-copd/) doi:10.1609/AAAI.V38I21.30520

BibTeX

@inproceedings{tyagi2024aaai-copd,
  title     = {{COPD-FlowNet: Elevating Non-Invasive COPD Diagnosis with CFD Simulations (Student Abstract)}},
  author    = {Tyagi, Aryan and Rao, Aryaman and Rao, Shubhanshu and Singh, Raj Kumar},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23671-23672},
  doi       = {10.1609/AAAI.V38I21.30520},
  url       = {https://mlanthology.org/aaai/2024/tyagi2024aaai-copd/}
}