SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection Using Multi-View Echocardiography

Abstract

Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is sub-stantial to prevent progressive damage to the myocardium. In this study, we propose a novel view-fusion model named self-attention fusion network (SAF-Net) to detect MI from multi-view echocardiography recordings. The proposed framework utilizes apical 2-chamber (A2C) and apical 4-chamber (A4C) view echocardiography recordings for classification. Three reference frames are extracted from each recording of both views and deployed pre-trained deep networks to extract highly representative features. The SAF-Net model utilizes a self-attention mechanism to learn dependencies in extracted feature vectors. The proposed model is computationally efficient thanks to its compact architecture having three main parts: a feature embedding to reduce dimensionality, self-attention for view-pooling, and dense layers for the classification. Experimental evaluation is performed using the HMC-QU-TAU11The benchmark HMC-QU-TAU dataset is publicly shared at the repository https://www.kaggle.com/aysendegerli/hmcqu-dataset. dataset which consists of 160 patients with A2C and A4C view echocardiography recordings. The proposed SAF-Net model achieves a high-performance level with 88.26% precision, 77.64% sensitivity, and 78.13% accuracy. The results demonstrate that SAF-Net model achieves the most accurate MI detection over multi-view echocardiography recordings.