Estimation affects of formats and resizing process to the accuracy of convolutional neural network
Muminov Bakhodir BoltaevichHead of the department of “Fundamentals of Informatics”, Tashkent University of Information Technologies, Tashkent, UzbekistanNasimov Rashid Hamid ogliSenior lecturer of “Computer systems” department, Tashkent University of Information Technologies, Tashkent, UzbekistanGadoyboyeva Nigora Soibjon qiziStudent of “Computer systems” department, Tashkent University of Information Technologies, Tashkent, UzbekistanMirzahalilov Sanjar Serkabay ogliSenior lecturer of “Computer systems” department, Tashkent University of Information Technologies, Tashkent, Uzbekistan
2019 International Conference on Information Science and Communications Technologies (ICISCT)conference2019en
ABI
Abstract
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.
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