ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds
This incremental improvement enhances diagnostic accuracy for cardiovascular health monitoring, benefiting medical professionals and patients.
The study tackled heart sound classification by developing ENACT-Heart, an ensemble method combining CNN and Vision Transformer via a Mixture of Experts framework, achieving 97.52% accuracy, which outperformed individual models.
This study explores the application of Vision Transformer (ViT) principles in audio analysis, specifically focusing on heart sounds. This paper introduces ENACT-Heart - a novel ensemble approach that leverages the complementary strengths of Convolutional Neural Networks (CNN) and ViT through a Mixture of Experts (MoE) framework, achieving a remarkable classification accuracy of 97.52%. This outperforms the individual contributions of ViT (93.88%) and CNN (95.45%), demonstrating the potential for enhanced diagnostic accuracy in cardiovascular health monitoring. These results demonstrate the potential of ensemble methods in enhancing classification performance for cardiovascular health monitoring and diagnosis.