Overall confusion matrices of all classifiers used in our study for RWMA detection performance results (multi-vies) with the optimized parameters by grid search 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 ChimaOverall confusion matrices of all classifiers used in our study for RWMA detection performance results (multi-vies) with the optimized parameters by grid search 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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