## 9 Discussion
[PERSON] and [PERSON] [50] used the Xception network, a pre-trained CNN model for automated steel surface classification. For the binary and multi-class classification, the classification accuracies were  
Figure 16: Confusion matrices for ablation studies of the proposed approach (multi-class classification (classes: (1) pitted surfaces, (2) crazing, (3) scratches, (4) patches, (5) multi-class defects)). 94% and 85%, respectively. [PERSON] et al. [51] performed automated surface steel detection with a specific GAN model. The binary class classification performance reached 96.80% accuracy. A hybrid approach with Faster R-CNN models and ResNet50 was proposed by [PERSON] et al. [21]. The prediction values obtained from weight activations of the ResNet model were utilized for the thresholding operation. If the scores were less than 0.3, the steel samples were considered to be defect-free. If scores were larger than 0.3, the samples were described as defective. With this method, the binary classification accuracy was 97.47%. Additionally, four copies of each steel sample image from the dataset were created, along with new class labels, using this method. The dataset was therefore multiplied by four. Steel surface flaw classification was conducted by [PERSON] et al. [52] using the Exception model. They achieved an accuracy of 88% for binary classification and the same accuracy of 88% for multiple classification. A combination of the ResNet model with the squeeze-and-excitation networks suggested by [PERSON] et al. [53] produced an accuracy value of 87.5% for the two-class classification task and 94% for the four-class classification problem. The efficacy of the proposed method was assessed by employing an alternate dataset, specifically the NEU Surface Defect Database [54]. Consequently, the dependability of the suggested approach was enhanced. This dataset comprised six distinct types of surface defects: crazing (class 1), inclusions (class 2), patches (class 3), pitted surfaces (class 4), rolled-in scales (class 5), and scratches (class 6). Through the utilization of 10-fold cross-validation, the dataset, consisting of a total of 1800 samples distributed evenly with 300 examples in each class, underwent evaluation. Within this dataset, the suggested technique demonstrated a classification accuracy of 99.77%, as depicted in the confusion matrix presented in Figure 17. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Methodology**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-4}  & & **Binary** & **Multi-Class** \\ \hline Proposed model & PI + PARC + INCA + SVM & 98.9 & 94.5 \\ [PERSON] et al. [21] & ResNet50 + Faster R-CNN & 97.47 & - \\ [PERSON] et al. [51] & GAN model & 96.8 & - \\ [PERSON] et al. [52] & Exception model & 87.6 & 85.0 \\ [PERSON] et al. [53] & SEResNET50 & 94.0 & 87.5 \\ [PERSON] et al. [50] & Transfer learning (Xception) & 94.0 & 85.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification accuracies of studies and the proposed model using the same dataset. Figure 17: Confusion matrix results of the proposed approach on the NEU Surface Defect Database. As can be seen from the results in Tables 4 and 5, the proposed model improved the classification performance compared to other models using the same dataset. However, given that the methods' training parameters and training-evaluation methodology differ, this finding does not necessarily imply that the proposed strategy is better than the others. Table 5 presents the performance results of the proposed approach and state-of-the-art models on the NEU Surface Defect Database. [PERSON] et al. [54] introduced a novel hybrid model, which underwent training from the ground up by incorporating the CNN architecture and five attention layers. The resultant CNN model, enriched with integrated attention, demonstrated an accuracy of 89.30%. Meanwhile, [PERSON] et al.