Enhancing Efficiency of Ejection Fraction Calculation in the Left Ventricle

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The calculation of the cardiac ejection fraction is important for determining whether or not a patient suffers from cardiovascular disease. However, manual calculation of the ejection fraction (EF) is prone to errors and is known to be prohibitively time-consuming. As such, there have been endeavors to automate this process for saving time as well as improving accuracy of estimation. Recently, GPU has been proposed to enhance the performance of machine learning algorithms. In addition, these algorithms are considered a necessary component in solving computational efficiency issues encountered in dealing with huge DICOM datasets. In this study, we used a DICOM dataset of cardiac MRI for 1200 human cases with different ages and gender to calculate the EF. Convolutional Neural Network (CNN) was the selected neural network for the training phase of segmenting the LV. Experiment target is enhancing efficiency of CNN to speedup training phase, and subsequently the prediction of the CVDs by experimenting with different GPU-based parallelism techniques, namely, Data Parallelism (DP) and Model Parallelism (MP) in addition to the generic use of multiple GPUs. Specifically, we performed four variants of experiments; the first was using GPUs with default behavior, the second two steps involve using either DP or MP alone, final variant involves combining both DP and MP. This was done on Amazon EC2 instances that support up to 8 GPUs per instance. We used two EC2 instances to apply our experiment on 16 GPUs. Our experiments show that our proposed combination of both DP and MP has the best computational efficiency. Precisely, a speedup of up to 9.88 (over a single GPU) was achieved when using 16 GPUs.