## 3 Results
### Experimental Setup
On the contrary, the data normalization resulted in a performance increase for detecting sick individuals with SVM, while it caused a performance decrease in identifying healthy individuals. The classification results for deep learning algorithms and MaxViT vision transformer with TL and FS are given in Table 4. The results were obtained by utilizing the independent test set. According to Table 4, it can be seen that the models with transfer learning outperformed the same models trained from scratch. The best accuracy, precision, F1-score, and MCC performances were achieved with the ResNet50 model. For the specificity evaluation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Sensitivity** & **Precision** & **Specificity** & **F1-Score** & **MCC** & **Epoch** \\ \hline AlexNet (TL) & 0.934 & 0.929 & 0.923 & 0.947 & 0.926 & 0.876 & 32 \\ \hline AlexNet (FS) & 0.833 & 0.800 & 0.790 & 0.855 & 0.795 & 0.654 & 50 \\ \hline ResNet50 (TL) & **0.945** & 0.929 & **0.935** & 0.956 & **0.932** & **0.886** & 16 \\ \hline ResNet50 (FS) & 0.757 & 0.794 & 0.669 & 0.733 & 0.726 & 0.517 & 60 \\ \hline VGG (TL) & 0.893 & 0.884 & 0.856 & 0.899 & 0.870 & 0.779 & 18 \\ \hline VGG (FS) & 0.854 & 0.819 & 0.819 & 0.877 & 0.819 & 0.697 & 44 \\ \hline RF-ResNet (proposed hybrid model) & 0.940 & **0.961** & 0.900 & 0.925 & 0.928 & 0.878 & N/A \\ \hline Effnetv2 (TL) & 0.903 & 0.825 & 0.927 & 0.956 & 0.873 & 0.799 & 60 \\ \hline Effnetv2 (FS) & 0.584 & 0.174 & 0.465 & 0.864 & 0.253 & 0.052 & 60 \\ \hline MaxVit (TL) & 0.926 & 0.871 & 0.944 & **0.964** & 0.906 & 0.848 & 60 \\ \hline MaxVit (FS) & 0.593 & 0.077 & 0.480 & 0.943 & 0.133 & 0.040 & 60 \\ \hline ConvNeXt-T (TL) & 0.870 & 0.871 & 0.818 & 0.868 & 0.844 & 0.733 & 60 \\ \hline ConvNeXt-T (FS) & 0.713 & 0.729 & 0.624 & 0.702 & 0.673 & 0.424 & 60 \\ \hline ConvNeXt-T AdamW & _0.969_ & 0.948 & _0.974_ & _0.983_ & _0.961_ & _0.935_ & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results for deep learning algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Sensitivity** & **Precision** & **Specificity** & **F1-Score** & **MCC** \\ \hline MLP & 0.598 & 0.213 & 0.556 & 0.877 & 0.308 & 0.121 \\ \hline KNN & 0.580 & 0.192 & 0.500 & 0.862 & 0.277 & 0.071 \\ \hline NB & 0.580 & 0.021 & 0.500 & **0.985** & 0.041 & 0.022 \\ \hline RF & **0.643** & 0.277 & **0.684** & 0.908 & 0.394 & **0.242** \\ \hline SVM & 0.616 & **0.447** & 0.553 & 0.739 & **0.494** & 0.193 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results for canonical machine learning algorithms with normalization. metric, the MaxVit model outperformed the other models. Additionally, the RF-ResNet50 hybrid model obtained the best sensitivity performance with 100 trees having a maximum depth of 3. Among the models evaluated, ResNet50 achieved the minimum number of epochs to complete the training process. The ConvNeXt-T model trained with the AdamW optimizer outperformed the other models in all metrics except for the sensitivity. AUC scores for deep learning and vision transformer models are given in Table 5.  
According to Table 5, the highest AUC score of 0.997 was obtained utilizing the ConvNeXt-T AdamW model. With an SGD optimizer, the highest AUC score of 0.987 was achieved by utilizing the AlexNet and ResNet50 models. The RF-ResNet50 hybrid model ranked third in terms of AUC. As can also be seen in the table, the models with transfer learning outperformed the same models trained from scratch. In the tables presenting the [PERSON] and McNemar's test results for statistical significance, bold values indicate a significant difference between the two models at the 5% level. Arrows further indicate which model achieved higher accuracy. If both a left arrow and an upward arrow are present next to a value, it should be understood that the two models have the same accuracy value.