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Estimation affects of formats and resizing process to the accuracy of convolutional neural network

机译:估计格式化和调整过程中的概述过程对卷积神经网络的准确性

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image quality, formatting, resizing and compression processes affect on deep neural network performance. These affects are investigated in many papers. However, signal represented images do not look like images which were captured by a camera, because they were plotted in a computer, which let to omit some noises. So formatting and resizing processes are important parameters that affect on network accuracy. In this work, ECG signal representation in different domains were saved in different image formats and CNN trained on these images. Obtained results were compared and showed that JPG image format best fits for training ECG images on CNN.
机译:图像质量,格式化,调整大小和压缩过程影响深度神经网络性能。这些影响在许多论文中进行了调查。然而,信号表示的图像看起来不像相机捕获的图像,因为它们被绘制在计算机中,这让省略一些噪音。因此,格式化和调整流程是影响网络准确性的重要参数。在这项工作中,不同域中的ECG信号表示以不同的图像格式保存,并在这些图像上培训CNN。比较了获得的结果,并显示了JPG图像格式最适合在CNN上训练ECG图像的最佳选择。

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