## 4 Results and Discussion  
To determine the success of the models following training, their performance in the assigned tasks was assessed by evaluating quantitative performance metrics including the accuracy, precision, sensitivity, F1-score, and loss. Additionally, the duration of the training sessions was subjected to analysis. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100 \tag{1}\]  
\[Precision=\frac{TP}{TP+FP}\times 100 \tag{2}\]  
\[Recall=\frac{TP}{TP+FN}\times 100 \tag{3}\]  
\[F1=\frac{TP}{TP+1/2(FP+FN)}\times 100 \tag{4}\]  
The definition of parameters used in (1), (2), (3), and (4) are given as follows:  
TP (True Positive): Correctly identifying something as true. When a known true value is correctly labeled as true. FP (False Positive): Incorrectly identifying something as true. When a known false value is incorrectly labeled as true. TN (True Negative): Correctly identifying something as false. When a known false value is correctly labeled as false. FN (False Negative): Incorrectly identifying something as false. When a known true value is incorrectly labeled as false. The size of the confusion matrix depends on the number of classes in the examples being considered. The value obtained from the loss function measures how different the model's predictions are from the actual values. High loss values suggest inaccurate predictions, while low values indicate accurate predictions. We used the N-fold cross-validation method to score our CNN model's success. We chose N = 5, which means we split the training set into five randomly selected equal parts and used each part N-1 times for training and one time for validation. The 5-fold method allowed us to test our CNN against overfitting and selection bias. Our model was fitted with 75 epochs (cycles) in each fold, and the best results obtained are shown in Figure 13. When the Training score: Epochs graph in Figure 13a is examined, it can be seen that our model learned regularly as the number of epochs increased. The training score, expressed by the blue dotted line, increased continuously, and became horizontal as it approached 75 epochs. The validation score, represented by the orange dotted lines, was significantly different from the training score at times. This was due to the inconsistent number of datapoints in the antibiogram disk classes in our dataset. To avoid this inconsistency, it would be appropriate to remove some classes from the dataset. After the 50 th cycle, the training score and the validation score remained proportional. The error matrix in Figure 14 shows quite large values in the classification rates of our classes. This was because these data were based on our best resulting fold. To determine the real success rate of our model, we summarized all five cross-validation results; statistical information from this is given in Table 3.  
We trained the VGG16 and DenseNet201 models on the same dataset with 100 epochs each. The other parameters were the same as for our CNN model. Tables 4 and 5 show the results of both transfer learning models. The DenseNet201 model had a 0.89 success rate, and VGG16 had a 0.91 success rate. Our CNN model worked slightly better on averaged data. Figure 13: (**a**) Training score: Epochs, (**b**) Training/Validation Loss: Epochs, (**c**) Learning Rate: Epochs.