O. I. Chumachenko, A. T. Kot, О. O. Voitiuk


It is considered a principle of thyroid pathology diagnostics intelligent system structure. It is determined basic ultrasound images features for patients with thyroid cancers. The block diagram of intellectual diagnostics is proposed. It includes two basic subsystems: making decision support and image processing. As a classifier it is used fuzzy neural networks (NEFCLASS) due to its synergy capabilities: rule-based representation and generalization possibilities. As a activation function of rule neuron (to calculate of activation of rules on the basis of membership functions) the T-norm is used. It is used convolution neural networks for ultrasound images processing.


Thyroid pathology diagnostics intelligent system; fuzzy neural networks; convolution neural networks.


G. E. Hinton and Vinod Nair, Rectrified Linear Units Improve Restricted Boltzmann Machines. 2011, pp. 56–66.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 28 July 2006.

G. E. Hinton, A practical guide to training restricted Boltzmann machines. (Tech. Rep. 2010-000). Toronto: Machine Learning Group, University of Toronto. 2010, pp. 160–169.

V. Katkovnik, A. Foi, K. Dabov, and K. Egiazarian, “Spatially adaptive support as a leading model selection tool for image filtering,” Proc. First Workshop Inf. Th. Methods Sci. Eng., WITMSE, Tampere, August 2008, pp. 365–457.

R. Gonsales and R. Woods, Digital image processing, Moscow: Technosphera, 2005, 635 p.

V. M. Sineglazov, E. I. Chumachenko, and V. S. Gorbatuk, Intellectual prediction methods, Kyiv: Osvita Ukraine, 2013, 236 p.

V. A. Soyfera, Methods of computer image processing, ed. V. A. Soyfera. Moscow: Fizmatlit, 2003, 698 p.

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