## 3 Results
### Experimental Setup
Wilcoxon test results for canonical machine learning algorithms without the data normalization and with the data normalization are given in Tables 6 and 7, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\(p\) **\(<\) 0.05**} & & & \\ \hline Classifier & kNN & MLP & SVM & RF & NB \\ \hline kNN & 1 & **0.041**\(\uparrow\) & 0.336 & 0.727 \\ \hline MLP & & **0.041**\(\uparrow\leftarrow\) & 0.297 & 0.617 \\ \hline SVM & & & 0.251 & **0.014**\(\uparrow\leftarrow\) \\ \hline RF & & & & 0.144 \\ \hline NB & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Wilcoxon test results without normalization. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **AUC** \\ \hline AlexNet (TL) & **0.987** \\ \hline AlexNet (FS) & 0.891 \\ \hline ResNet50 (TL) & **0.987** \\ \hline ResNet50 (FS) & 0.842 \\ \hline VGG-16 (TL) & 0.961 \\ \hline VGG-16 (FS) & 0.922 \\ \hline EffNetv2 (TL) & 0.973 \\ \hline EffNetv2 (FS) & 0.614 \\ \hline MaxViT (TL) & 0.982 \\ \hline MaxViT (FS) & 0.519 \\ \hline ConvNeXt-T (TL) & 0.957 \\ \hline ConvNeXt-T (FS) & 0.535 \\ \hline RF-ResNet50 Hybrid & **0.984** \\ \hline ConvNeXt-T AdamW & **0.997** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC Scores for deep learning models. According to Table 6, there is a statistical significance between the results of the kNN and SVM, MLP and SVM, and SVM and NB models. The results show that the SVM model achieves statistically significant superiority in classification performance. According to Table 7, there is a statistical significance between the results of the kNN and SVM, kNN and NB, MLP and SVM, MLP and NB, SVM and RF, SVM and NB, and RF and NB models. The results show that the SVM model achieves statistically significant superiority in classification performance. [PERSON]'s test results for canonical machine learning algorithms without the data normalization and with the data normalization are given in Tables 8 and 9, respectively. According to Tables 8 and 9, there is no statistical significance among the canonical machine learning models. Data normalization also eliminated the statistically significant differences observed between the models in their unnormalized state. [PERSON] test results for deep learning algorithms are given in Table 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\) **\(<\) 0.05** & & & \\ \hline Classifier & kNN & MLP & SVM & RF & NB \\ \hline kNN & \(1\) & **0.0009 \(\uparrow\)** & 0.819 & **0.0003 \(\uparrow\leftarrow\)** \\ \hline MLP & & **0.0001 \(\uparrow\)** & 0.819 & **0.0002 \(\leftarrow\)** \\ \hline SVM & & & **0.0009 \(\uparrow\)** & **0** \(\leftarrow\)** \\ \hline RF & & & & **0.0002 \(\leftarrow\)** \\ \hline NB & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilcoxon test results with normalization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(p\) **\(<\) 0.05** & & & \\ \hline Classifier & kNN & MLP & SVM & RF & NB \\ \hline kNN & & 0.617 & 0.505 & 0.108 & 1 \\ \hline MLP & & & 0.695 & 0.251 & 0.637 \\ \hline SVM & & & & 0.602 & 0.505 \\ \hline RF & & & & 0.127 \\ \hline NB & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: [PERSON]’s test results with normalization. According to Table 10, Resnet50 and ConvNext-T with AdamW models have statistically significant superiority in classification performance. There is a statistical significance between the results of the AlexNet and VGG, EffNetv2, ConvNeXt-T, ConvNeXt-T with AdamW, RF-ResNet50 hybrid; ResNet50 and VGG, EffNetv2, ConvNeXt-T, ConvNeXt-T with AdamW, RF-ResNet50 hybrid; VGG and ConvNeXt-T with AdamW, RF-ResNet50 hybrid; EffNetv2 and ConvNeXt-T with AdamW, RF-ResNet50 hybrid; ConvNeXt-T and ConvNeXt-T with AdamW, RF-ResNet50 hybrid; ConvNeXt-T with AdamW and RF-ResNet50 hybrid models. According to Table 11, ConvNext-T with AdamW and the RF-ResNet50 hybrid models have statistically significant superiority in classification performance.