TRAINING DATA SAMPLING FOR CONVENTIONAL NEURAL NETWORKS CONFIGURING

Abstract

The problem of generating training data for setting up the convolutional neural networks is considered, which is of great importance in the construction of intelligent medical diagnostic systems, where due to the lack of elements of the training sample, it is proposed to use the approaches of artificial data multiplication based on the initial training sample of a fixed size for the image processing (the results of the ultrasound, CT and MRI). It shows that the increase of the training sample resulted in less informative and poor quality elements, which can introduce extra errors in the goal achievement. To eliminate this situation the algorithm for assessing the quality of a sample element with the subsequent removal of uninformative elements is proposed.