# Generative Adversarial Networks in Retinal Image Classification
## 4 Experimental Analysis
It is computed as follows:  
\[Recall=\frac{tp}{tp+fn}\]  
where _fn_ indicates the number of false negatives. F-Measure, or F1 score, is the harmonic mean of precision and recall. It provides a balanced measure that takes into account both precision and recall. The F-Measure considers the trade-off between precision and recall, giving equal importance to both metrics. It is computed as the weighted average of precision and recall, where the weights are determined by their relative importance. It is computed as follows  
\[F\text{-}Measure=2*\frac{Precision*Recall}{Precision+Recall}\]  
By evaluating the Precision, Recall, and F-Measure of the classifiers, the authors aimed to gain insights into their effectiveness in distinguishing between real and fake retina images generated by the DCGAN. These metrics provide a comprehensive understanding of the classifiers' performance in terms of accuracy, true positive rate, and the balance between precision and recall. Four different widespread supervised machine learning classifiers are exploited with the aim of enforcing conclusion validity: J48 [18], SVM [19], Random Forest [20], and Bayes [21]. In the experiment, the authors built a separate model for each algorithm and for each epoch, resulting in a total of 200 different models. Specifically, there were four algorithms considered and each algorithm had a model built for every epoch. To construct each model, a combination of real-world application images and synthetic images generated by the DCGAN for a specific epoch was used. This means that, for each epoch, the researchers had a dataset that included both real retina images from real-world applications and synthetic retina images generated by the DCGAN. By creating multiple models for each algorithm and epoch, the researchers aimed to analyze the performance and effectiveness of the classifiers in distinguishing between real and fake retina images at different stages of the training process. This approach allows any changes in the classifier performance to be observed as the DCGAN generated images that were presumably becoming more similar to real retina images over the course of the training epochs. Below, we explain how we built and evaluated the machine learning models designed to differentiate between real and fake retina images. Relating to the model learning, we consider \(T\) as a set of labels \(\{(M,l)\}\), where each \(M\) is the label that is associated with an \(l\in\{\textit{real,fake}\}\). For the \(M\) model, we build a numeric vector of features \(\text{F}\in R_{y}\), where \(y\) represents the number of features exploited in the learning phase (\(y=64\); as a matter of fact, this is the number of numeric features obtained, from each image, by applying the Simple Color Histogram Filter). In more detail, with respect to the training phase, \(k\)-fold cross-validation is exploited. We explain this process: the instances of the dataset are split in a random way into a set denoted as \(k\). In order to test the effectiveness of both the models we propose, the procedure explained below is considered:  
1. Generation of a set for the training, i.e., \(T\subset D\);
2. Generation of an evaluation set \(T^{\prime}=D\div\text{T}\);
3. Execution of the model training \(T\);
4. Application of the model previously generated to each element of the \(T^{\prime}\) set. To mitigate the risk of overfitting, we employed cross-validation in their evaluation process. Cross-validation ensures that all samples in the dataset are evaluated during the testing phase. Below we explain how we accomplished cross-validation:  
1. _Data Splitting_: The entire dataset, consisting of real and fake retina images, was divided into k equal-sized parts or folds. The value of k determines the number of subsets the dataset is divided into. 2. _Training and Validation Iteration:_ During each iteration of the cross-validation process, one of the folds was designated as the validation set, while the remaining k \(-\) 1 folds were used as the training set.