Classification of echocardiographic standard views using a hybrid attention-based approach

Xianda, Ni and Zi, Ye and Jaya Kumar, Yogan and Goh, Ong Sing and Fengyan, Song (2022) Classification of echocardiographic standard views using a hybrid attention-based approach. Intelligent Automation and Soft Computing, 33 (2). pp. 1197-1215. ISSN 1079-8587

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Abstract

The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. However, classifying echocardio-grams at the video level is complicated, and previous observations concluded that the most significant challenge lies in distinguishing among the various adjacent views. To this end, we propose an ECHO-Attention architecture consisting of two parts. We first design an ECHO-ACTION block, which efficiently encodes Spatio-temporal features, channel-wise features, and motion features. Then, we can insert this block into existing ResNet architectures, combined with a self-attention module to ensure its task-related focus, to form an effective ECHO-Attention network. The experimental results are confirmed on a dataset of 2693 videos acquired from 267 patients that trained cardiologist has manually labeled. Our methods provide a comparable classification performance (overall accuracy of 94.81%) on the entire video sample and achieved significant improvements on the classification of anatomically similar views (precision 88.65% and 81.70% for parasternal short-axis apical view and parasternal short-axis papillary view on 30-frame clips, respectively).

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, Attention mechanism, Classification, Echocardiogram views
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	04 Jul 2024 11:12
Last Modified:	04 Jul 2024 11:12
URI:	http://eprints.utem.edu.my/id/eprint/27129
Statistic Details:	View Download Statistic

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