Dependence of the performance quality of neural networks on the characteristics of training data when working with thyroid ultrasound images

E.A. Ekhlakov, K.V. Tsyguleva, M.E. Dunaev, S.M. Zakharova


The purpose of this work is to test the hypothesis that the performance of neural network models for the detection and segmentation of nodular formations in thyroid ultrasound images is practically independent of the number of analyzed images of one patient obtained at the same time. Two deep architectures were used for verification: YOLOv5 when solving the detection problem and DeepLabV3 when solving the segmentation problem. During the experiments, video loops (frame sequences) of thyroid ultrasound were used, containing more than 7000 images of transverse and longitudinal projections of 166 patients. The performance of deep architectures was evaluated both at the stage of their training and validation, and at the stage of their testing. According to the results of the experiments, it was found that an increase in the number of similar images in loops with a constant number of patients under study does not affect the operation of deep architectures.

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