## 3 Data and Methods
### CNNs
#### 3.1.2 Performance Metrics for Binary Classification  
The choice of neural network performance metrics is an integral part of creating any model. The main purpose of metrics is to monitor and measure the performance of the model during its training and testing. They indicate weaknesses in the model and dataset, the elimination of which leads to an increase in the generalization ability of the final model. Depending on the objectives and specifics of the research, different metrics are used that are characteristic of a particular field of science. Frequently the choice of method of evaluating model quality is based on the experience of researchers in the same field, as well as on the experience of the researchers themselves. In the case of binary classification, metrics such as accuracy, precision, recall, F1 score, binary cross-entropy loss, receiver operator characteristic and area under the curve (ROC-AUC), and the Matthews correlation coefficient (MCC) are used to evaluate the performance of the neural network [27]. In this paper, the following metrics were chosen to evaluate the performance of the created CNN models: accuracy and the binary cross-entropy loss function. Both metrics are often used when debugging classifiers with two classes [28]. One of the necessary conditions for the correct use of accuracy as an evaluation metric is the presence of a balanced dataset. In this study, this condition is completely fulfilled.  
Accuracy is the most widely spread measure of performance, which describes the overall prediction accuracy of a model across all classes. It is calculated as the ratio between the number of correct class predictions to the total number of predictions. In other words, the metric is the fraction of correct predictions of the trained model. Accuracy is measured as a percentage, ranging from 0% to 100%. A higher value of this metric corresponds to a better-performing classifier, while a lower one indicates more errors in class predictions.  
For binary classification, accuracy can be defined in terms of positives and negatives according to the following equation [29]:  
\[ACC=\frac{TP\ +TN}{TP\ +TN\ +FP\ +FN}, \tag{1}\]  
where _TP_ (true positive) is the number of correctly classified positive samples; _TN_ (true negative) is the number of correctly classified negative samples; _FP_ (false positive) is the number of misclassified positive samples; and _FN_ (false negative) is the number of misclassified negative samples.  
The binary cross-entropy loss function is a metric in machine learning that is used to evaluate how well a classification model with two classes performs. This function takes two values, actual and predicted, and returns the comparative evaluation that shows how close the predicted probability is to the actual value (0 or 1). Thus, the loss function rewards the model for giving a correct prediction with a low loss and penalizes for a wrong outcome with a high error value. In the ideal case, the value of binary cross-entropy loss is zero. But a loss of zero indicates overfitting. This means that the model learns, along with the necessary features, noise or random fluctuations in the data, which negatively affects the model's ability to generalize. Therefore, in practice, the main goal throughout the training of a CNN is to minimize the loss value, i.e., to minimize the difference between the predicted probability and true label in a final model.  
Binary cross-entropy is a special case of cross-entropy [30], and is determined by the following formula:  
\[BCEloss=-\frac{1}{N}{\sum_{i=1}^{N}}[y_{i}log(p_{i})+(1-y_{i})log(1-p_{i})], \tag{2}\]  
where \(N\) is the number of final layer outputs (1 for sigmoid); \(y_{i}\) is the binary indicator (0 or 1) denoting the class for the sample, \(i\); and \(p_{i}\) is the classifier's predicted probability distribution.