Image processing with liver mri convolutional neural network

Authors

  • V. M. Sineglazov National Aviation University
  • V. P. Hotsyanivskyy National Aviation University

DOI:

https://doi.org/10.18372/1990-5548.50.11384

Keywords:

Automation deep learning, image processing, medicine

Abstract

The methods of learning machine learning models to analyze magnetic resonance imagingimages and identify them on the disease

Author Biographies

V. M. Sineglazov, National Aviation University

Doctor of Engineering Science. Professor.Educational and Scientific Institute of Information and Diagnostic Systems, Aviation Computer-Integrated Complexes Department

V. P. Hotsyanivskyy, National Aviation University

Student. Educational and Scientific Institute of Information and Diagnostic Systems, Aviation Computer-Integrated Complexes Department

References

M. Riesenhuber, “Object recognition in cortex: Neural mechanisms, and possible roles for attention,” Department of Neuroscience Georgetown University Medical Center, Washington, DC, 2007.

Y. Lecun and Y. Bengio, Convolutional Networks for Images, Speech, and Time-series, in Arbib, M. A. (Eds), The Handbook of Brain Theory and Neural Networks, MIT Press, 1995.

Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE, 1998.

Downloads

Issue

Vol. 4 No. 50 (2016)

Section

THEORY AND METHODS OF SIGNAL PROCESSING

License

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).