Image processing of ct with help of a convolutional neural network

Authors

  • V. M. Sineglazov National Aviation University
  • M. O. Omelchenko National Aviation University

DOI:

https://doi.org/10.18372/1990-5548.50.11385

Keywords:

Deep learning, image processing, medicine, artificial neural network, computer tomography

Abstract

The methods of learning Artificial Neural Network to analyze computed tomographyimages 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

M. O. Omelchenko, National Aviation University

Student. Educational and Scientific Institute of Information and Diagnostic Systems

References

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.

Y. Tay, P. Lallican, M. Khalid, C. Viard-Gaudin, and S. Knerr, “An Offline Cursive Handwriting Word Recognition System”, Proc. IEEE Region 10 Conf., 2001.

Springhouse corporation. illustrated guide to diagnostic tests. Springhouse, pa: springhouse corporation, 1998.

Kenji Suzuki, Feng Li, Shusuke Sone, and Kunio Doi, “Computer-Aided Diagnostic Scheme For Distinction Between Benign And Malignant Nodules In Thoracic Low-Dose Ct By Use Of Massive Training Artificial Neural Network”, IEEE Transactions On Medical Imaging, 2005.

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How to Cite

Sineglazov, V. M., & Omelchenko, M. O. (2016). Image processing of ct with help of a convolutional neural network. Electronics and Control Systems, 4(50), 41–45. https://doi.org/10.18372/1990-5548.50.11385

Issue

Vol. 4 No. 50 (2016)

Section

THEORY AND METHODS OF SIGNAL PROCESSING

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