# Generative Adversarial Networks in Retinal Image Classification
## 4 Experimental Analysis
This process was repeated k times, with each fold taking turns as the validation set. 3. _Model Training and Evaluation:_ For each iteration, a separate model was trained using the training set. The performance of the model was then evaluated using the validation set. The evaluation metrics, such as Precision, Recall, and F-Measure, were calculated based on the model's predictions on the validation set. 4. _Performance Aggregation:_ The performance of the model was assessed across all k iterations. The individual performance scores from each iteration were aggregated to obtain an overall estimate of the model's performance. Typically, this aggregation is performed by calculating the mean of the performance scores across the iterations. In this study, the researchers chose a value of k = 10, which means the dataset was divided into 10 equal parts and the iteration was repeated 10 times. Each fold served as the validation set once, while the remaining nine folds were used for training the model. The final performance metrics were calculated as the average of the metrics obtained from the 10 iterations. This approach helps to mitigate overfitting and provides a more realistic evaluation of the classifiers' performance in distinguishing between real and fake retina images. In Table 3 we show the experimental analysis results: for the reason of space we report the results related to three epochs: the first one (i.e., 0 in the column _Epoch_), the middle one (i.e., 25 in the column _Epoch_), and the final one (i.e., 49 in the column _Epoch_), with the aim of understanding the general trend. From Table 3 it emerges that when the number of epochs is increasing the metrics, i.e., Precision, Recall, and F-Measure, suffer a decrease: for instance at epoch 0 the SVM F-Measure is equal to 0.989, at epoch 25 it is equal to 0.954, and at epoch 49 it is equal to 0.936. We also note that this trend is not reflected in the J48 model: as a matter of fact, at epoch 0 the J48 model F-Measure is equal to 1, at epoch 25 it is equal to 0.970, and at epoch 49 it is equal to 0.971. So we can say that from epoch 25 to epoch 49 the J48 performances remain substantially unchanged. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epoch** & **Algorithm** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{4}{*}{0} & J48 & 1 & 1 & 1 \\  & SVM & 0.990 & 0.989 & 0.989 \\  & RandomForest & 1 & 1 & 1 \\  & Bayes & 0.999 & 0.999 & 0.999 \\ \hline \multirow{4}{*}{25} & J48 & 0.970 & 0.970 & 0.970 \\  & SVM & 0.956 & 0.954 & 0.954 \\  & RandomForest & 0.974 & 0.974 & 0.974 \\  & Bayes & 0.958 & 0.957 & 0.957 \\ \hline \multirow{4}{*}{49} & J48 & 0.972 & 0.971 & 0.971 \\  & SVM & 0.939 & 0.936 & 0.936 \\ \cline{1-1}  & RandomForest & 0.974 & 0.973 & 0.973 \\ \cline{1-1}  & Bayes & 0.956 & 0.955 & 0.955 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental analysis results for epochs 0, 25, and 49. A trend similar to the one obtained from the SVM classifier is shown in the RandomForest model, in fact; at epoch 0 the F-Measure is equal to 1, at epoch 25 it is equal to 0.974, and at epoch 49 it is equal to 0.973. A similar consideration can be made with respect to the Bayes model, with an F-Measure equal to 0.999 at epoch 0, an F-Measure of 0.957 at the 25-th epoch, and an F-Measure equal to 0.955 with regard to epoch 49. This decreasing trend obtained when the epoch number is increasing is something that is expected, considering that the GAN learns to build better (fake) retina images at each epoch: but, as we noticed in the results shown in Table 3, the performance decay is minimal but still present. Thus, the series of retinal images fail to be distinguished correctly by the classifier.