Congenital Heart Disease recognition using Deep Learning/Transformer models
Journal:
arXiv
Published Date:
May 13, 2025
Abstract
Congenital Heart Disease (CHD) remains a leading cause of infant morbidity
and mortality, yet non-invasive screening methods often yield false negatives.
Deep learning models, with their ability to automatically extract features, can
assist doctors in detecting CHD more effectively. In this work, we investigate
the use of dual-modality (sound and image) deep learning methods for CHD
diagnosis. We achieve 73.9% accuracy on the ZCHSound dataset and 80.72%
accuracy on the DICOM Chest X-ray dataset.
