## 4 Discussion
### AUC-ROC Analysis  
This study used the AUC-ROC (area under the receiver operating characteristic curve) metric to evaluate and compare deep learning models, because in the unbalanced class database, accuracy alone cannot indicate the true performance of the model, as a model that predicts the dominant class may have high accuracy but poor performance in identifying rare classes. This metric is one of the most useful for evaluating classification model performance, especially when class distributions are uneven. AUC-ROC computes the area under the ROC curve for all possible choice criteria and transforms it into a single integer. This score shows the model's overall ability to distinguish between positive and negative data, regardless of a specific threshold. The AUC-ROC values range from 0 to 1, with values around 1 indicating excellent model performance and values of 0.5 or lower indicating poor random performance. In addition to the AUC-ROC, the Precision, Recall, F1-Score and Balanced Accuracy metrics are provided to round out the model evaluation (Tables 5 and 6). Since this study was conducted on the CODEBRIM dataset, which involves multi-class and multi-label classification, it is important to state the difference between standard AUC and micro-average AUC (Mic Avg AUC). Per-class AUC measures the area under the ROC curve for each class separately. This value indicates how well the model is able to detect a particular defect. Instead of calculating it separately for each class, micro-average AUC (Mic Avg AUC) calculates the sum of the TP, FP, TN and FN across all classes and then obtains the AUC value. This method is more efficient in situations where there are unbalanced data. Considering that our data contain defects with an unbalanced distribution, Mic Avg AUC is a more appropriate index to evaluate the overall ability of models to detect real defects in real-world conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MetaQNN [24]** & **VGG16 [30]** & **Inception-V3 [30]** & **ResNet-50 [30]** & **Resnet-18 This Study** \\ \hline Accuracy\% & 72.19\% & 88.00\% & 89.00\% & 90.00\% & 90.90\% \\ \hline Model & Resnet-50 & Resnet-101 & ResNeXt-50 & ResNeXt-101 & Efficient-B3 \\  & This study & This study & This study & This study & This study \\ \hline Accuracy\% & 92.50\% & 93.10\% & 93.60\% & 93.50\% & 93.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative study of model accuracies using CODEBRIM data. Figure 10 presents the ROC curves for multiple deep learning models, illustrating their classification performance. The \(x\)-axis represents the False Positive Rate (FPR), while the \(y\)-axis denotes the True Positive Rate (TPR). Among the tested architectures, the ResNet models (ResNet-18, ResNet-50 and ResNet-101) exhibit strong classification performance, but their effectiveness varies across defect types. For instance, the \"exposed bars\" class consistently achieves a high AUC, close to 1, across all the models. However, classes like \"corrosion stain\" and \"spalling\" show relatively lower AUC values in some ResNet variants, indicating that these models struggle to differentiate them from similar defect patterns. The ResNeXt series (ResNeXt-50 and ResNeXt-101) shows improvements over ResNet, primarily due to grouped convolutions, which enhance feature extraction and representation. ResNeXt-101, in particular, achieves an AUC above 0.96 for most classes and even reaches 0.99 for certain defect types, demonstrating superior classification accuracy. Among all the models, EfficientNet-B3 emerges as the best-performing architecture. Its AUC remains above 0.96 across all classes, and for some categories, it reaches a close score of 1. This suggests that EfficientNet-B3 is highly effective in distinguishing structural defects, providing a robust decision boundary while maintaining computational efficiency compared to deeper ResNet and ResNeXt models.