DIAGNOSTIC SYSTEM BASED ON AUTOENCODERS

Authors

  • V. M. Sineglazov National Aviation University, Kyiv, Ukraine
  • S. V. Kostiuchenko National Aviation University, Kyiv, Ukraine

DOI:

https://doi.org/10.18372/1990-5548.55.12771

Keywords:

Image, recognition, autoencoders, neural network, layer, convolution, perceptron, training

Abstract

It`s considered the problem of image processing which is used in diagnostic systems when it is necessary to process the results of ultrasound, computed tomography and magnetic resonance imaging. For the solution of this problem it`s often used artificial neural networks especially convolution neural networks. It`s considered the structure of convolutional neural networks especially types of layers. In the paper it is analyzed the creation of convolutional neural networks based on autoencoders. It`s considered the features of such neural network, the algorithm of learning and the important parameters which determine the function quality of image processing. The possible improvement of such topology is possible with help of restricted Boltzmann machine which can be used for pre-learning.

Author Biographies

V. M. Sineglazov, National Aviation University, Kyiv, Ukraine

Aviation Computer-Integrated Complexes Department, Education&Scientific Institute of Information-Diagnostics Systems

Doctor of Engineering Science. Professor

S. V. Kostiuchenko, National Aviation University, Kyiv, Ukraine

Aviation Computer-Integrated Complexes Department, Education&Scientific Institute of Information-Diagnostics Systems

Bachelor

References

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Issue

Vol. 1 No. 55 (2018)

Section

THEORY AND METHODS OF SIGNAL PROCESSING

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