## 9 Discussion  
In this section, ablation studies for the proposed approach were performed and the proposed approach was compared with state-of-the-art methods. Figures 15 and 16 show the effect of processed images (PI) and residual attention strategies on classification accuracy. Descriptive summary information about these ablation studies is provided in Table 3 for binary and multi-class classification, respectively. As seen in Figure 15 for the binary classification, the best accuracy was obtained by the proposed approach (PI + PARC + SVM) while the worst accuracy was obtained by the raw image (RI) + CNN strategy (Figure 15a). In Figure 15b, PI instead of RI was used for the classification and the classification accuracy was improved by 3.5%. In Figure 15c, the attention structure (PI + A-CNN) was added to the CNN model in Figure 15b. The classification accuracy was improved by 0.4%. In Figure 15d, the residual structure  
Figure 14: ROC curves and AUC values of the proposed approach: multi-class classification (classes: (**a**) pitted surfaces, (**b**) crazing, (**c**) scratches, (**d**) patches, (**e**) multi-class defects). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model Name** & **Model Info** & **Acc (a)** & **Acc (b)** \\ \hline RI + CNN & Raw images + PARC model without attention and residual structures & 93.00\% & 87.20\% \\  & Processed images + PARC model & 93.50\% & 89.20\% \\ PI + CNN & without attention and residual structures & 93.90\% & 89.90\% \\ PI + A-CNN & Processed images + PARC model without residual structures & 93.90\% & 89.90\% \\ PI + R-CNN & Processed images + PARC model without attention structures & 94.50\% & 91.20\% \\  & Processed images + PARC model (no feature selection with INCA and no SVM classifier) & 95.40\% & 91.90\% \\  & Processed images + PARC model & 96.80\% & 93.90\% \\ PI + PARC + SVM & Proposed approach & 98.30\% & 97.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summarizing information about the ablation study for the proposed approach ((**a**) binary classification, (**b**) multi-class classification). (PI + R-CNN) was added to the CNN model in Figure 15b. The classification accuracy was improved by 1.0%. In Figure 15e, the parallel residual and attention structure (PI + PARC) was added to the CNN model in Figure 15b. The classification accuracy was improved by 1.5%. In Figure 15f, the SVM classifier with INCA feature selection algorithm was applied in place of the softmax classifier in the proposed Parallel Attention-Residual CNN (PARC) model and the accuracy of classification was improved by 2.9% compared to the model in Figure 15e. As seen in Figure 16 for the multi-class classification, the best accuracy was obtained by the proposed approach (PI + PARC + SVM) while the worst accuracy was obtained by the raw image (RI) + CNN strategy (Figure 16a). In Figure 16b, PI instead of RI was used for the classification, and the accuracy performance of classification was increased by 2.0%. In Figure 16c, attention structure (PI + A-CNN) was added to the CNN model in Figure 16b. The classification accuracy was improved by 0.7%. In Figure 16d, the residual structure (PI + R-CNN) was added to the CNN model in Figure 16b. The classification accuracy was improved by 2.0%. In Figure 16e, the parallel residual and attention structure (PI + PARC) was added to the CNN model in Figure 16b. The classification accuracy was improved  
Figure 15: Confusion matrices for ablation studies of the proposed approach (binary classification (classes 1: defect 2: non-defect)). by 2.7%. In Figure 16f, the SVM classifier with the INCA algorithm was used instead of the softmax classifier in the suggested PARC method, and the accuracy performance of classification was improved by 5.6% in comparison with the model in Figure 16e. Table 4 presents the performance results of the proposed approach and state-of-the-art models using the dataset named \"Severstal: Steel Defect Detection\".