  We propose a method of searching for radio sources exhibiting the
Sunyaev-Zeldovich effect in the multi-frequency emission maps from the Planck
mission data using a convolutional neural network. A catalog for recognizing
radio sources is compiled using the GLESP pixelation scheme at the frequencies
of 100, 143, 217, 353, and 545 GHz. The quality of the proposed approach is
evaluated and the quality of the dependence of model data on the S/N ratio is
estimated. We show that the presented neural network approach allows the
detection of sources with the Sunyaev-Zeldovich effect. The proposed method can
be used to find the most likely galaxy cluster candidates at large redshifts.
