Abstract
The problem of forming a training set for the task of image processing is considered. It is shown that this task is of great importance in the construction of intelligent medical diagnostic systems in which convolution neural networks are used for image processing (results of ultrasound, CT and MRI). Due to the lack of elements of the training sample, it is proposed, on the one hand, to use approaches of artificial data multiplication based on the initial training sample of a fixed volume, and on the other hand, to use methods that reduce the need for large training samples, both through the use of ensemble topology (hybrid neural networks), and by applying the transfer learning approach. An algorithm for the formation of a training set for image processing tasks is developed based on the modification of the initial input information with the calculation of the confidence measure of the obtained sample.
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