The purpose of this study is to develop a fuzzy expert system based on the analysis of biomedical images for the diagnosis of oncological diseases using breast cancer as an example. The main directions of application of mathematical methods in medical diagnosis are analyzed, their drawbacks are evaluated, and principles of diagnosis, based on fuzzy logic, are formulated. Mathematical models and algorithms are developed, formalizing the process of diagnostic decision-making based on fuzzy logic with quantitative and qualitative parameters of the patient's condition; mathematical models of membership functions are developed, formalizing the representation of quantitative and qualitative parameters of the patient's condition in the form of fuzzy sets, used in models and algorithms for diagnosing and determining the diagnosis in the case of breast cancer.
The methods of processing biomedical images, namely thermal images, are investigated. Algorithms for calculating the temperature and area of the zone of interest in the manual mode operator-computer, as well as in the automatic mode, are specified. Methods of thermal image processing are presented, namely recursive generalized contour preparation and preparation based on histograms of connections. An experimental study of these methods was performed, as well as a comparison of thermal image segmentation methods in manual segmentation modes, using contour preparation-based segmentation, multilevel segmentation based on recursive generalized contour preparation, and automatic segmentation based on connectivity histograms.
KEYWORDS: Interference (communication), Signal processing, Fractal analysis, Digital signal processing, Statistical analysis, Heart, Data modeling, Analytical research, Telecommunications, Diagnostics
The article explores the possibility of applying modifications to the R/S- analysis method for pulsogram processing under conditions where the noise level is unknown. It is shown that a fast algorithm for calculating the Hirst coefficient can be used to estimate the noise level in a pulse signal. The R/S-analysis method for pulsogram processing has been improved by optimizing the initial conditions, which makes it possible to select the required pulse wave registration interval.
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