TRAINING DATA SAMPLING FOR CONVENTIONAL NEURAL NETWORKS CONFIGURING

Authors

  • V. M. Sineglazov National Aviation University, Kyiv
  • A. T. Kot National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.66.15225

Keywords:

Сonvolution neural networks, artificial reproduction of data, uninformative data, intelligent medical systems

Abstract

The problem of generating training data for setting up the convolutional neural networks is considered, which is of great importance in the construction of intelligent medical diagnostic systems, where due to the lack of elements of the training sample, it is proposed to use the approaches of artificial data multiplication based on the initial training sample of a fixed size for the image processing (the results of the ultrasound, CT and MRI). It shows that the increase of the training sample resulted in less informative and poor quality elements, which can introduce extra errors in the goal achievement. To eliminate this situation the algorithm for assessing the quality of a sample element with the subsequent removal of uninformative elements is proposed.

Author Biographies

V. M. Sineglazov, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department

Faculty of Air Navigation Electronics and Telecommunications

 

A. T. Kot, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Post-graduate student

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[No3] O. I. Chumachenko and A. T. Kot. "Formation of a Learning Set for the Task of Image Processing," Electronics and Control Systems, N 3(65), Kyiv, NAU: Osvita Ukrainy, pp. 9–17, 2020. https://doi.org/10.18372/1990-5548. 65.14978

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Issue

Vol. 4 No. 66 (2020)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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