journal article·2025Open access

A Transformer Based on Feedback Attention Mechanism for Diagnosis of Coronary Heart Disease Using Echocardiographic Images

Chunlai Du, Xin Gu, Yanhui Guo, Siqi Guo, Ziwei Pang, Yi Du, Guoqing Du

Computers, Materials & Continua· Volume 83· Issue 2

Abstract

Coronary artery disease is a highly lethal cardiovascular condition, making early diagnosis crucial for patients. Echocardiograph is employed to identify coronary heart disease (CHD). However, due to issues such as fuzzy obje... | Find, read and cite all the research you need on Tech Science Press
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References (31)

Show source-provided references
1. Cardiovascular Diseases [Internet]; 2024. [cited 2024 Sep 5]. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
2. Recent advances in artificial intelligence-assisted ultrasound scanning10.3390/app13063693 ↗
3. Enhancing medical diagnosis through deep learning and machine learning approaches in image analysis
4. Deep learning in medical ultrasound analysis: a review10.1016/j.eng.2018.11.020 ↗
5. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence
6. Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks
7. Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy10.1161/CIRCIMAGING.115.004330 ↗
8. Fast and accurate view classification of echocardiograms using deep learning10.1038/s41746-017-0013-1 ↗
9. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population10.1007/s12350-014-0027-x ↗
10. Machine learning for pre-diction of all-cause mortality in patients with suspected coronaryartery disease: a 5-year multicentre prospective registry analysis
11. Automatic apical view classification of echocardiograms using a discriminative learning dictionary10.1016/j.media.2016.10.007 ↗
12. Deep residual recurrent neural networks for characterisation of cardiac cycle phase from echocardiograms
13. Recognizing end-diastole and end-systole frames via deep temporal regression network
14. Why resnet works? Residuals generalize10.1109/TNNLS.2020.2966319 ↗
15. Long short-term memory with activation on gradient10.1016/j.neunet.2023.04.026 ↗
16. Cardiac phase detection in echocardiograms with densely gated recurrent neural networks and global extrema loss10.1109/TMI.2018.2888807 ↗
17. Detection of cardiac events in echocardiography using 3D convolutional recurrent neural networks
18. Multibeat echocardiographic phase detection using deep neural networks10.1016/j.compbiomed.2021.104373 ↗
19. Sofka · Deep learning in medical image analysis and multimodal learning for clinical decision support · 2017 · 258
20. Real-time standard view classification in transthoracic echocardiography using convolutional neural networks10.1016/j.ultrasmedbio.2018.07.024 ↗
21. Lei Ba J, Kiros JR, Hinton GE. Layer normalization. arXiv:1607.06450. 2016.
22. Swin transformer: hierarchical vision transformer using shifted windows
23. Deep residual learning for image recognition
24. Designing network design spaces
25. Searching for MobileNetV3
26. EfficientNetV2: smaller models and faster training
27. A convnet for the 2020s
28. Efficientnet: rethinking model scaling for convolutional neural networks
29. MobileNetV2: inverted residuals and linear bottlenecks
30. Going deeper with convolutions
31. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. 2014.