# Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma
## 1 Introduction  
The modern level of knowledge in the field of information technologies allows creating quite effective tools for optimizing the processes of medical diagnosis and treatment, in particular, the diagnosis of glaucoma. However, in order to increase the effectiveness and reliability of support for decision-making about the level of pathology in the diagnosis of glaucoma, it is necessary to create automated systems based on a wide range of modern mathematical and optimization methods, systems theory and system analysis, in particular, the use of the fuzzy set method. Today, this problem in medical practice is quite non-trivial, since the factor of subjectivity enters the decision-making process, which leads to a decrease in the reliability of the established diagnosis. An early diagnosis of eye pathologies is the most important stage in the treatment of eye diseases, in particular, fundus, glaucoma, etc. At the same time, inaccurate vision test results can lead to the appointment of inappropriate treatment, and instead of helping and restoring vision, it will only worsen it.  
When experts form a reference sample of images of fundi, conflicts may arise due to ambiguities in the images. Practice has shown that even the same expert can differently assess whether the same image displays glaucoma or not at different times (after a week, a month, etc.). It is natural that different experts often have different opinions about the presence of glaucoma in images of fundi. Therefore, when forming a knowledge base using an expert system, it is necessary to provide procedures for assessing the convergence and reproducibility of the results of creating a reference sample [1; 2; 3].  
The result of the work on an optical-electronic expert system is a conclusion about the presence of glaucoma of a certain type with an indication of its probability. Thelatter requires the creation of a representative reference sample of images of a necessary volume [4; 5; 6].  
A feature of the considered expert system is that, along with the knowledge of experts accumulated in it, a database is created based on the results of measuring quantitative features through automated image processing. Next, a conclusion is formed based on the assessment of the probability of the considered image exhibiting a type of glaucoma belonging to a list of possible types [2; 7; 8; 9].  
Since conclusions based on the results of drug research using expert systems are formed based on the results of computer image processing, it is necessary to consider the factors affecting the accuracy of the measurements performed in the system [10; 11; 12; 13].  
For the considered task of automating the analysis of fundi, in the first stage, image improvement procedures are performed; these are associated with the suppression of image-distorting factors (filtering of obstacles, elimination of lighting irregularities, etc.). In the description stage, the characteristic features of an object are calculated; on this basis, in the third stage, the object is assigned to a class. The key to these three stages is the description stage. The recognition result depends on the choice of features and their informativeness (the ability to attribute an object to a class based on the value of the features). We can distinguish two groups of factors that influence the recognition result; the first group is that of the properties of the object itself (images of fundi are very diverse), while the second group is that of the image formation conditions (sensor noise, uneven illumination of the object, etc.) [14; 15].  
The purpose of this work is to develop a conceptual model of an optical-electronic expert system for the diagnosis of glaucoma using computer processing methods, as well as to analyze the influences of factors on the results of fundus image recognition [16; 17; 18].