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

Autor:	Adalioglu, Ilke, Ahishali, Mete, Degerli, Aysen, Kiranyaz, Serkan, Gabbouj, Moncef
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial 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-TAU 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 the SAF-Net model achieves the most accurate MI detection over multi-view echocardiography recordings. Comment: 4 pages, 3 figures, Computing in Cardiology (CinC) 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.15520 Zobrazit plný text záznamu View this record from Arxiv