Determination of Oncourological Pathologies Based on the Analysis of Medical Images Using Machine Learning Methods

Abstract

The chapter presents a method for diagnosing oncourological diseases based on machine learning algorithms. MobileNet50, ResNet50 convolutional neural networks are used to solve the problem of classifying patient biopsy image segments according to the Gleason scale. Augmentation technologies were applied to the existing data set for better performance of the neural network. The accuracy of the algorithm was estimated by the total error and the Cohen's Kappa coefficient. The results of the algorithm in software show a good level of accuracy: in 65% of cases, the algorithm accurately determined the Gleason index, and the rest of the data had a slight deviation of the confusion matrix.