## 2 Development of the Algorithm
### Dataset Structure
In clinical practice, misclassifications between the absence of disease (N), early-stage AMD (S), and intermediate-stage AMD (P) could result in a risk of overlooking the onset of AMD. This situation may require adjustments in patient management strategies. Additionally, misclassifying geographic atrophy cases as earlier stages, with eight cases incorrectly categorized as stage S, could delay essential interventions. To improve the efficiency of CNN, the parameters of convolutional layers and their impact on classification accuracy were studied. Since biomarkers in different stages of AMD vary in size, the impact of convolution kernel size was investigated in terms of accuracy in separating S and V classes. A neural network with convolutional and fully connected layers was used for the study. The results, showing the relationship between the model's sensitivity (true positive rate, TPR) and convolution kernel size, are presented in Figure 2 after cross-validation. The data indicate that the sensitivity of CNNs with an average kernel size increased by at least 8% compared to the previously used \(5\times 5\) kernel size. The results of the cross-validation process showed a connection between the levels of biomarkers linked to different stages of AMD and the model's sensitivity when using different convolution parameters. These findings were used to create a framework that simultaneously processes information through multiple convolution layers, each with its unique convolution kernels. We explored how the encoder's performance varies when employing parallel convolution layers at each stage. The results of this study are presented in a confusion matrix, as shown in Figure 3 and summarized in Table 2.  
We used statistical significance tests and effect size calculations to compare the performance of two models: a primary encoder and an encoder with parallel convolutional layers. To evaluate the effectiveness of parallel convolutional layers, we employed a nonparametric McNemar test to compare the two models on the same dataset [61]. We used a Cohen effect size measure, h, to estimate the extent of changes in the models' results for each class [62]. Figure 1: Error matrix of the primary encoder model. Figure 2: Sensitivity of the model for classes S and V when using different sizes of convolution kernels. The results of the statistical significance tests, including the effect size, are presented in Table 3.  
The analysis of the test results from the statistical evaluation of the two models shows that integrating parallel convolutional layers into the encoder structure resulted in a statistically significant improvement for class P. This is supported by the findings of the McNemar test (\(p<0.05\)) and an average Cohen coefficient of h = 0.237. However, further modifications to the classifier were necessary to ensure a consistent improvement in model performance for the other classes. These adjustments also aimed to address the issue of significant class imbalance and improve the accuracy of class P differentiation from classes S and V. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **N** & **P** & **S** & **SI** & **V** & **VI** \\ \hline \multirow{2}{*}{Precision} & 0.974 & 0.6915 & 0.6465 & 0.5349 & 0.6406 & 0.6897 \\  & (0.96, 0.98) & (0.66, 0.72) & (0.61, 0.67) & (0.51, 0.56) & (0.61, 0.67) & (0.66, 0.71) \\ \hline \multirow{2}{*}{Sensitivity} & 0.7075 & 0.6842 & 0.7529 & 0.7419 & 0.7069 & 0.6667 \\  & (0.67, 0.73) & (0.65, 0.71) & (0.72, 0.78) & (0.71, 0.76) & (0.67, 0.73) & (0.63, 0.69) \\ \hline \multirow{2}{*}{Specificity} & 0.9939 & 0.9147 & 0.900 & 0.9505 & 0.939 & 0.952 \\  & (0.98, 1.0) & (0.89, 0.93) & (0.88, 0.91) & (0.93, 0.96) & (0.92, 0.95) & (0.93, 0.96) \\ \hline \multirow{2}{*}{F1-score} & 0.8197 & 0.6878 & 0.6957 & 0.6216 & 0.6721 & 0.678 \\  & (0.79, 0.84) & (0.65, 0.71) & (0.66, 0.72) & (0.59, 0.65) & (0.64, 0.7) & (0.64, 0.72) \\ \hline \hline \end{tabular}
\end{table}