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Classification of Echocardiographic Standard Views Using a Hybrid Attention-based Approach

Zi Ye1, Yogan Jaya Kumar2, Goh Ong Sing2, Fengyan Song3, Xianda Ni4,*

1 School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China
2 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
3 Shanghai Gen Cong Information Technology Co. Ltd., Shanghai, 201300, China
4 Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325003, China

* Corresponding Author: Xianda Ni. Email:

Intelligent Automation & Soft Computing 2022, 33(2), 1197-1215.


The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. However, classifying echocardiograms 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).


Cite This Article

Z. Ye, Y. Jaya Kumar, G. Ong Sing, F. Song and X. Ni, "Classification of echocardiographic standard views using a hybrid attention-based approach," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 1197–1215, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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