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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 the sake of saving time as well as improving accuracy of estimation. Recently,GPUhave been proposed to enhance the performance of machine learning algorithms that attempt to estimate the EF. In addition, these algorithms are considered a necessary component in solving computational efficiency issuesencountered in dealing with hugeDigital Imaging and Communications in Medicine (DICOM)datasets. In this study, we useda DICOM dataset of cardiac magnetic resonance imaging for 1200 human cases with different ages and gender to calculate the ejection fraction in the left ventricle.Convolutional Neural Network (CNN) was the selected neural network for the training phase of segmenting the LV and volume calculation. Our target is enhancing efficiencyof 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 without applying any control on its behavior, the second two variants involve experiments using either DP alone or MP alone on multiple GPUs, while the fourth and 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 havethe bestcomputational efficiency. Precisely, a speedup of up to 9.88 (over a single GPU) was achieved when using 16 GPUs in parallel with combined DP and MP.
How to Cite
, N. A. K.-E. A. E. (2018). Enhancing Effeciency of Ejection Fraction Calculation in the Left Ventricle. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(1), 69–73. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/966