## 4 Results and Discussion
### Comparison of Proposed Models Among Themselves
It was followed by Inception-v3, ResNet-18, EfficientNet-b0, VGG16, the proposed MSU-CNN, GoogLeNet, and AlexNet, respectively. Figure 8: Comparison of classifier F1-scores using 5-fold CV. Figure 6: ROC curves with 5-fold CV for the ResNet-50 model. Figure 7: Comparison of classifier accuracies using 5-fold CV. The precision values of the classification models are illustrated in Figure 9. Precision indicates the accuracy of the model's positive predictions. In other words, it evaluates the proportion of instances predicted as positive that are actually positive. The AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models achieved precision values of 0.9553, 0.9712, 0.9668, 0.9791, 0.9878, 0.9901, 0.9916, and 0.9935, respectively. According to these results, the ResNet-50 model is the best in indicating the accuracy of the model's positive predictions. It is followed by Inception-v3, ResNet-18, EfficientNet-b0, VGG16, GoogLeNet, the proposed MSU-CNN, and AlexNet, respectively. The sensitivity values of the classification models are illustrated in Figure 10. Sensitivity reflects the model's ability to accurately detect true positives. In other words, it quantifies the proportion of actual positive instances that are correctly identified. The AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models achieved sensitivity values of 0.9426, 0.9639, 0.9678, 0.9784, 0.9894, 0.9899, 0.9923, and 0.9984, respectively. According to these results, the ResNet-50 model has the highest ability to correctly identify true positives. Then, in the sensitivity ranking, Inception-v3, ResNet-18, EfficientNet-b0, VGG16, the proposed MSU-CNN, GoogLeNet, and AlexNet models come, respectively. Figure 10: Comparison of classifier sensitivities using 5-fold CV. Figure 4.0 Comparison of classifier precisions using 5-fold CV. The specificity values of the classification models used are presented in Figure 4.1 These specificity values reflect the ability of the model to correctly detect negative cases. The AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models achieved specificity values of 0.9426, 0.9639, 0.9678, 0.9784, 0.9894, 0.9899, 0.9923, and 0.9956, respectively. According to these specificity results, ResNet-50 model is the best in distinguishing true negatives. Then, Inception-v3, ResNet-18, EfficientNet-b0, VGG16, the proposed MSU-CNN, GoogLeNet, and AlexNet come next in specificity order, respectively. The ROC-AUC values of the models are presented in Figure 4.2 AUC represents the area under the ROC curve. It ranges from 0 to 1 and serves as a single metric summarizing the model's performance. An AUC of 1 indicates a perfect model, meaning it distinguishes all classes without error. The AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 models achieved AUC values of 0.9936, 0.9957, 0.9952, 0.9982, 0.9995, 0.9993, 0.9997, and 0.9997, respectively. According to these results, the ResNet-50 and Inception-v3 models separate the classes with fewer errors compared to the others. It is followed by EfficientNet-b0, ResNet-18, VGG16, GoogLeNet, the proposed MSU-CNN, and AlexNet, respectively. Figure 4.3 Evaluation of specificity of classifiers using 5-fold CV. Figure 4.4 Comparison of classifier ROC-AUC values using 5-fold CV. In this study, data augmentation was applied to the training dataset. Data augmentation only includes random rotation, random horizontal translation and random vertical translation transformation operations. Other transformation methods such as horizontal symmetry or vertical symmetry, random scaling were tried but good results were not obtained. As a result of the trials, random rotation was set to be rotated between \(-10\) and \(+10\) at random angles, random horizontal scrolling and random vertical scrolling were set to be shifted between \(-5\) and \(+5\) pixels to the right or left.