## 3 Results and Discussion  
The results of the training and testing of the machine-learning experiments are collected in Table 3. As can be observed, even though experiment 1 was performed with a higher number of images (19,200) compared with those involved in experiment 2 (1200), the training accuracy was worse in all the six ML algorithms for experiment 1. From these results, it can be inferred that, in this type of classification problem, the results are improved with better image resolution. Although there is a significant percentage of success in the pre-trained step, the accuracy was low for all the models. Thus, the best accuracy model for the first experiment corresponded to the Ensemble Boosted Trees method with a value of 70.6% for the training and a poor 39.0% for the test accuracy. The second experiment, with better resolution images and much less training time, also produced bad results, finding the Naive Bayes (Gaussian) as the best model with a testing accuracy of 46.1%. Thus, these models cannot be generalized, and they present overfitting. Thus, the models will fail to present accurate predictions with new images. On the other hand, the training progress of the transfer learning experiments, conducted on the pre-trained networks GoogLeNet and ResNet50, are shown in Figures 6 and 7, respectively. The loss function progress is depicted as well in Figures 6 and 7. It can be observed that two epochs would have been sufficient, since the third epoch barely gives a significant improvement in the accuracy obtained--that is, the third epoch indicates that the accuracy cannot be improved any further. of reliability, while recall is used to represent the fraction of samples that were correctly recognized--that is, the model's detection capability [34]. The harmonic mean of recall and precision is included as well as the average value of all indicators. Finally, [PERSON]'s correlation coefficient was included as an indicator of the imbalance sensitivity of the process. The classification was balanced since the same number of images per class were used to train and validate the ML classifiers and the transfer-learning networks. Logically, the values obtained for [PERSON]'s coefficient equal almost 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **Specificity** & **F1 (Har. Mean)** & **MCC** \\ \hline Annealing & 0.9968 & 0.9859 & 0.9984 & 0.9914 & 0.9871 \\ Quenching & 0.9846 & 1 & 0.9922 & 0.9922 & 0.9884 \\ Quench. + Tem & 1 & 0.9953 & 1 & 0.9977 & 0.9965 \\ Average & 0.9938 & 0.9938 & 0.9969 & 0.9938 & 0.9907 \\ \hline \hline \end{tabular} Precision = TP/(TP + FP); Recall = TP/(TP + FN); Specificity = TN/(TN + TP); F1 = Harmonic mean = 2 (precision \(\times\) recall)/(precision + recall); and MCC = [PERSON]’s correlation coefficient.  
\end{table} 
Table 4: Indicators that define the behavior of pre-trained deep-learning network GoogLeNet; FP/FN = false positives/false negatives; and TP/TN = true positives/negatives. These values were obtained from samples not used for training and/or validating the network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **Specificity** & **F1 (Har. Mean)** & **MCC** \\ \hline Annealing & 0.9917 & 1 & 0.9962 & 0.9959 & 0.9883 \\ Quenching & \(1\) & 0.9924 & \(1\) & 0.9962 & 0.9919 \\ Quench. + Tem & 1 & 1 & 1 & 1 & 0.9965 \\ Average & 0.9972 & 0.9975 & 0.9987 & 0.9973 & 0.9922 \\ \hline \hline \end{tabular} Precision = TP/(TP + FP); Recall = TP/(TP + FN); Specificity = TN/(TN + TP); F1 = Harmonic mean = 2 (precision \(\times\) recall)/(precision + recall); and MCC = [PERSON]’s correlation coefficient.  
\end{table} 
Table 5: Indicators that define the behavior of pre-trained deep-learning network ResNet50; FP/FN = false positives/false negatives; and TP/TN = true positives/negatives. These values were obtained from samples not used for training and/or validating the network. Figure 7: Accuracy and loss of training progress in the ResNet50 transfer learning.