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Myocardial infarction (MI) is a severe case of coronary artery disease (CAD)and ultimately, its detection is substantial to prevent progressive damage tothe myocardium. In this study, we propose a novel view-fusion model namedself-attention fusion network (SAF-Net) to detect MI from multi-viewechocardiography recordings. The proposed framework utilizes apical 2-chamber(A2C) and apical 4-chamber (A4C) view echocardiography recordings forclassification. Three reference frames are extracted from each recording ofboth views and deployed pre-trained deep networks to extract highlyrepresentative features. The SAF-Net model utilizes a self-attention mechanismto learn dependencies in extracted feature vectors. The proposed model iscomputationally efficient thanks to its compact architecture having three mainparts: a feature embedding to reduce dimensionality, self-attention forview-pooling, and dense layers for the classification. Experimental evaluationis performed using the HMC-QU-TAU dataset which consists of 160 patients withA2C and A4C view echocardiography recordings. The proposed SAF-Net modelachieves a high-performance level with 88.26% precision, 77.64% sensitivity,and 78.13% accuracy. The results demonstrate that the SAF-Net model achievesthe most accurate MI detection over multi-view echocardiography recordings.

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