Analysis of using software packages for imaging in medical diagnostics

Authors

  • V. M. Sineglazov National Aviation University
  • D. S. Raduchych National Aviation University

DOI:

https://doi.org/10.18372/1990-5548.50.11399

Keywords:

Automation, deep learning, image processing, medicine, neural networks, software

Abstract

The most popular software packages were analyzed using artificial neural networks. LibrariesTreano and Torch were investigated

Author Biographies

V. M. Sineglazov, National Aviation University

Doctor of Engineering Science. Professor. Educational and Scientific Institute of Information and Diagnostic Systems

D. S. Raduchych, National Aviation University

Student. Educational and Scientific Institute of Information and Diagnostic Systems

References

V. D. Kustikova and P. N. Druzhkov, “A Survey of Deep Learning Methods and Software for Image Classification and Object Detection.” In: Proc. of the 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding, 2014.

Y. Le Cun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision.” In: Proc. of the IEEE Int. Symposium on Circuits and Systems (ISCAS), 2010, pp. 253–256.

M. Hayat, M. Bennamoun, and S. An, “Learning Non-Linear Reconstruction Models for Image Set Classification.” In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2014.

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Issue

Vol. 4 No. 50 (2016)

Section

MATHEMATICAL MODELING OF PROCESSES AND SYSTEMS

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