AAAI 2024technical0 citations
COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations (Student Abstract)
Aryan Tyagi, Aryaman Rao, Shubhanshu Rao, Raj Kumar Singh
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.
BibTeX
@article{Tyagi_Rao_Rao_Singh_2024, title={COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30520}, DOI={10.1609/aaai.v38i21.30520}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tyagi, Aryan and Rao, Aryaman and Rao, Shubhanshu and Singh, Raj Kumar}, year={2024}, month={Mar.}, pages={23671-23672} }