# Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma
## 7 Evaluation of Metrological Indicators
* Then, P\({}_{\text{2c}}\) = P\({}_{\text{choice}}\)\(\pm\) tm = 12% \(\pm\) 2 \(\times\) 5.01 = 12% \(\pm\) 10.02%. * Then, P\({}_{\text{3c}}\) = P\({}_{\text{choice}}\)\(\pm\) tm = 48% \(\pm\) 2 \(\times\) 7.71 = 48% \(\pm\) 15.42%. Taking the results of the calculation of the confidence limits of the average value of the general population (R\({}_{\text{gen}}\)) into account, we established with the probability of an error-free forecast (\(p\) = 95%) that the frequency of detection of stage I glaucoma at the age of 50-65 years was in the range from 27.72% to 58.28% of cases (diagnosis by an ophthalmologist) and from 24.88% to 55.12% (diagnosis obtained using an expert database). The frequency of detection of stage II glaucoma at the age of 50-65 years ranged from 3.30% to 24.70% of cases (diagnosis by an ophthalmologist) and from 1.98% to 22.02% (diagnosis obtained with an expert database). The frequency of detection of stage III glaucoma at the age of 50-65 years ranged from 27.72% to 58.28% of cases (diagnosis by an ophthalmologist) and from 32.58% to 63.42% (diagnosis obtained with an expert database). The main purpose of this study is to diagnose glaucoma and to assess whether the disease has affected people. The model can generate either positive or negative results depending on whether the glaucoma disease impact assessment is achieved. The ability of a model to correctly predict the behavior of a subset is known as its accuracy. Efficiency is calculated using it during the classification process. Its purpose is to give a rough idea of how often both positive and negative changes are needed. In the classification of glaucoma fundus images, it was demonstrated that the training and testing of the proposed model was more accurate than previous models. The results of this study are shown in Table 3.  
When evaluating a model, its F-measure, accuracy, precision, and specificity are all considered. Inception additional deep learning models, such as ResNet, ResNet 152v4, DenseNet-169, and VGG-19, CNN are now being utilized to analyze Proposed Model Fuzzy Logic classification findings. For example, in glaucoma fundus image classification, training and testing using the proposed model turned out to be more accurate than other models previously. The results of this study are shown in Figure 5 and Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\boldsymbol{\mathscr{M}}\) & **Models** & **Training** & **Testing** \\ \hline
1 & VGG-19 & 97.73 & 95.54 \\ \hline
2 & Inception ResNet & 94.86 & 91.64 \\ \hline
3 & ResNet 152v2 & 97.56 & 93.21 \\ \hline
4 & DenseNet169 & 97.14 & 95.45 \\ \hline
5 & U-Net & 98.11 & 96.37 \\ \hline
6 & CNN & 98.82 & 96.90 \\ \hline
7 & Proposed Model Fuzzy Logic & 99.1 & 97.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: An analysis of the effectiveness of the accuracy, on the base [28]. 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. In the future, the received data will be processed by blocks of fuzzy logic, which will make it possible, having in its resource the data of the knowledge base and the received image of a specific patient, to conduct an analysis and give a result about this or that disease. This will enable the doctor to make a correct conclusion about the condition of the fundus during the diagnosis of glaucoma.