# Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma
## 8 Conclusions  
This paper proposes a deep method based on fuzzy sets for glaucoma assessment and prediction. The glaucoma dataset was used to train a glaucoma image analysis learning model. To detect glaucoma, the images were classified using a deep convolutional neural network.  
These images of the fundus retina were used to determine whether the patient had glaucoma. The resulting image data were compared to the baseline images of the dataset. The model was used to extract features from segmented images for data processing.  
For the first time, mathematical models were obtained for the evaluation of biomedical images using fuzzy set methods on the basis of expert knowledge bases, which made it possible to carry out complex qualitative diagnostics and increase the reliability and efficiency of diagnosis. A methodology for analyzing biomedical images based on the fuzzy set apparatus was formed, which allowed a fuller assessment of the level of disease for glaucoma.  
Aspects of the practical implementation of an optical-electronic system of biomedical information processing were considered. An algorithm and an optical-electronic system for biomedical image analysis were proposed, and they were used to increase the informativeness and reliability of the diagnosis of eye pathologies--in particular, glaucoma. Compared to previous methods, this increased training accuracy from an average of 1.09 percent to 3.96 percent. Compared to other considered models, the tests are 97.2 percent accurate, which means a performance gain of 0.3 to 5.56 percent.  
On the basis of the developed model and algorithms, a hardware and software implementation was created, and experimental and medical studies of the obtained system indicators were conducted.  
Examples of practical applications of the developed optical-electronic system for the analysis of eye pathologies are given. The main practical result is that the expediency and reliability of such an optical-electronic system were practically confirmed.  
The results of this study can be generalized to a variety of imaging modalities. The proposed method of transfer of learning has an additional advantage, as it can be applied in various areas of medicine and biology.  
Figure 5: Comparative analysis of the testing and training accuracy of the proposed model with traditional models.  
**Author Contributions:** Conceptualization, O.M. and S.P.; methodology, S.P.; software, O.M.; formal analysis, date base, Y.S.; mathematical models, K.M.; resources, Y.S.; algorithm, mathematic analysis, S.Z.; data curation, K.M.; writing--original draft preparation, S.P.; writing--review and editing, S.P. and K.M.; project administration, O.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP 19675574).  
**Institutional Review Board Statement:** The study was approved by the Bioethics Committee at the 475 National Pirogov Memorial Medical University, Vinnytsia, Ukraine (Resolution No. 8, 22 October 2020).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.