The motion feature extraction part for the Temporal ConvNet backbone employed for echocardiograms.
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posted on 2024-09-12, 17:31 authored by Sazzli Kasim, Junjie Tang, Sorayya Malek, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff, Jesvinna Kaur ChimaEach of the K optical flow format echocardiogram snippets is classified using one Temporal ConvNet instance. The horizontal(X) and vertical(Y) Optical flow fields were extracted 5 frame each time. The first convolution layer is adjusted to 224×224×10 to accept. The snippet-level echocardiograms c (s;) are aggregated in the consensus layer, yielding the overall RWMA detection. Snippets are stacks of several optical flow fields calculated between consecutive echocardiogram frames. Temporal ConvNets backbone on all snippets share same parameters.
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single echocardiographic cyclemotion field featuresmethod &# 8217heart &# 8217diastolic phases withincycle cardiac data95 %, reinforcing61 %, precision13 %, specificitytemporal segment networktemporal convolutional networksdeep learning classifiersmethodology synergizes uclassifier also attainedapproach may overlookintegrating machine learningenhance detection accuracyoptical flow algorithmslv contour annotationsa4c views echocardiogramsreliable rwma detectionmachine learningoptical flow52 %,early detectionoverall accuracyinnovative approachincorporates ualso contributestemporal convnetxlink ">tsn ),successful treatmentstudies usingqu datasetsput forwardprecise toolpeak valuesnovel techniquemyocardial infarctionleft ventricularkey framesglobal leaderfold crossf1 scoreearly indicatordisplacement curvesdetect rwma39 %.
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