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Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography

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Figure

Figure 1 The four different types of neural network architectures used in this study, along with the lowest error rate of each model within each group
Figure 2 The 14 echocardiographic views. A2CH, apical 2 chamber; A3CH, apical 3 chamber; A4CH, apical 4 chamber; A5CH, apical 5 chamber; Ao, aorta; AV, aortic valve; IAS, interatrial septum; LA, left atrium; LV, left ventricle; PLAX, parasternal long axis; PS, parasternal; PV, pulmonary valve; RA, right atrium; RV, right ventricle; TV, tricuspid valve.
Table 1 Baseline characteristics of study-level and video-level parameters
Table 2 Table demonstrating the total number of trainable parameters, accuracy on the test set, and associated Cohen’s kappa for each of the networks on the test set
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