## 4 Result Exploration and Argument  
Through the confusion matrix, we can find out the P, R, F1, and Accuracy. These are the criteria for evaluating the classification model. Confusion matrix has four keywords of this including True Positive, False Positive, False Negative, and True Negative [48]. We can define **Precision**, **Recall**, **F1-score**, and **Accuracy** mathematically by using the Equations (1)-(4) respectively. \[Precision,P=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall,R=\frac{TP}{TP+FN} \tag{2}\]  
\[F1-score,F1=2\times\frac{P\times R}{P+R} \tag{3}\]  
\[Accuracy=\frac{Number\ of\ correct\ predictions}{Total\ number\ of\ predictions\ made} \tag{4}\]  
After 30 epochs of training of all TL models, an evaluation is made on a test dataset consisting of 8001 images where 4000 images are cracks and 4001 images are non-cracks. Figure 12 illustrates the confusion matrix of all models. The TL VGG16 predicts 3996 (TP) cracks and 3990 (TN) non-cracks images correctly, as well as 4 (FN) cracks images predicted as non-crack, and 11 (FP) non-cracks images predicted as cracks Figure 12a. The number of FN is minimum among other models. Figure 11: Loss and accuracy of all TL models both in Train and validation. The TL ResNet18, on the other hand, predicts 3988 (TP) cracks and 3959 (TN) non-cracks images correctly, as well as 12 (FN) cracks images predicted as non-crack and 42 (FP) non-cracks images predicted as cracks Figure 12b. In this case, the number of FP is the highest among other models. We can see in the TL DenseNet161 model's confusion matrix Figure 12c, that it predicts 3994 (TP) cracks and 3981 (TN) non-cracks images correctly, as well as 8 (FN) cracks images predicted as non-crack and 20 (FP) non-cracks image predicted as cracks. TL AlexNet Figure 12d shows the balance between FN and FP and shows the minimum number of FP (3 FP) among all other models. Table 3 displays several standard assessment scores, with the number of samples utilized during the evaluation represented in the Support column which is denoted as Sup. From Table 3, we can conclude that TL AlexNet achieves the highest 99.86% F1 scores and 99.86% accuracies among other models and precision of 99.92% on cracks and recall of 99.93% on non-cracks. In the case of popular statistical tests named MCC and CK (Cohen's Kappa), AlexNet performs better than others. The values of MCC and CK are almost the same, we took 4 digits after the decimal point. Although, AlexNet achieves the best validation accuracy of 99.90% during the 13 th epoch training, shown in Table 4.  
As a succinct outline of every TL algorithm throughout training as well as validation, Table 4 displays the highest, lowest, and average accuracy. From the summary, we can conclude that AlexNet models achieve the best train accuracy of 99.85% on the 24 th epoch and the best validation accuracy of 99.90% on the 13 th epoch among all other models. Table 5 shows the training duration of each epoch during training on Google Colaboratory GPU runtime. The TL AlexNet achieves 1 st place by taking minimum training time among the other models. Figure 12: Confusion matrix of TL VGG16, TL ResNet18, TL DenseNet161, and TL AlexNet. We also depicted the receiver operating characteristic (ROC) curve for comparing the models in the case of appropriate classification results. It is measured based on the performance of the false positive rate and true positive rate respectively. Figure 13 shows the ROC curve of different TL models in our works. In this figure, we denote four curves of red, green, blue, and orange colored for AlexNet, DenseNet, VGG16, and ResNet18 models. All models' performances are good. On the left side of this figure, all curves are looking together. For understanding better, we observed it as zoom out which is shown in the right portion of the figure. From this figure, we can see that AlexNet places the highest position over other models. We also presented another way of evaluating the performance of models named the precision-recall (PR) curve.