用CNN和Transformer融合模型提升心跳音诊断准确率
ENACT-Heart -- ENsemble-based Assessment Using CNN and Transformer on Heart Sounds
- 采用专家混合框架融合CNN与视觉变压器
- 在心音分类中达到97.52%准确率，超越单一模型
- 适合心血管疾病智能诊断研究者参考
本研究探索了视觉变换器（ViT）原理在音频分析中的应用，聚焦于心音识别。本文提出ENACT-Heart——一种基于CNN与ViT互补优势的集成方法，通过专家混合（MoE）框架实现97.52%的分类准确率，显著优于单独使用ViT（93.88%）或CNN（95.45%）的表现，证明了集成方法在心血管健康监测与诊断中的潜力。
原文摘要 · Abstract (English)
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.
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