Mean AUC for RWMA detection performance results (multi-vies) computed over the test sets of 5−fold in HMC-QU dataset.
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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 ChimaMean AUC for RWMA detection performance results (multi-vies) computed over the test sets of 5−fold in HMC-QU dataset.
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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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