Congenital Heart Disease recognition using Deep Learning/Transformer models
This work addresses CHD detection to assist doctors, but it appears incremental as it applies existing deep learning methods to new medical data.
The paper tackled the problem of non-invasive screening for Congenital Heart Disease (CHD) by investigating dual-modality deep learning methods, achieving 73.9% accuracy on the ZCHSound dataset and 80.72% accuracy on the DICOM Chest X-ray dataset.
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.