This article analyzes the pathological conditions of the breast based on the study of cytological images. Cytological images are a separate class of biomedical images and are used in the diagnosis of cancer. For diagnose precancerous and cancerous conditions and treatment tactics, diagnosticians use cytological, histological, and immunohistochemical images. For automating the process of diagnosis in oncology, automated microscopy systems are used. Automated microscopy systems use computer vision algorithms. Recently, machine learning algorithms have been used to classify images. Microscopic image processing is a complex and time-consuming process, as the images are characterized by high noise levels and the absence of clear contours of cell nuclei. To calculate the quantitative characteristics of cell nuclei cytological images, the method for calculating the quantitative characteristics of cell nuclei based on image filtering algorithms and their automatic segmentation has been developed. An U-Net convolutional neural network architecture has been developed for cell nucleus segmentation. In this work, the method of processing cytological images is developed. The method consists of six stages. The first step is to load the image into the computers memory. In the second stage, the images are preprocessed. The third stage is the automatic segmentation of images based on the convolutional neural network of the U-Net type. In the fourth stage, the quantitative characteristics of cell nuclei are calculated. In the fifth stage, the quantitative characteristics of the cell nuclei are stored in a database. In the sixth stage, linear regression algorithms are used to obtain quantitative characteristics of cell nuclei. Currently, linear regression is one of the common approaches of machine learning to data analysis. In this work, the comparative analysis of the quantitative characteristics application in cell nuclei is carried out based on linear regression. The scientific novelty of the work is development the method for calculating the quantitative characteristics of cell nuclei, which includes stages of image filtering and automatic segmentation based on the use of a neural network such as U-Net. The practical significance of the work is the software implementation of the preprocessing modules and linear regression. In particular, investigated that the set of parameters "area, length of the main axis" has 1.4 times less RMSE error compared to the set "area, perimeter".
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