## 4 Results
The performances of algorithms are evaluated in terms of precision, recall, F1-score, accuracy, and MCC metrics, and the results are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{\ \begin{tabular}{c} **BD** \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **SCH** \\ **(\%)** \\ \end{tabular}  } & \multicolumn{4}{c}{**Accuracy (\%)**} & \multicolumn{4}{c}{**AUC (\%)**} & \multicolumn{4}{c}{**MCC (\%)**} \\ \cline{5-12}  & & & **Mean** & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** & **Min** & **Max** \\ \hline \multirow{3}{*}{ResNet} & **Prec. ** & 77.42 & 77.11 & \multirow{3}{*}{60.53} & \multirow{3}{*}{91.71} & \multirow{3}{*}{81.44} & \multirow{3}{*}{61.79} & \multirow{3}{*}{98.26} & \multirow{3}{*}{52.77} & \multirow{3}{*}{22.91} & \multirow{3}{*}{83.53} \\  & **Recall** & 76.26 & **76.52** & & & & & & & & \\  & **F1 Sc. ** & 75.98 & 76.02 & & & & & & & & \\ \hline \multirow{3}{*}{ResNet + Grad-CAM} & **Prec. ** & 75.72 & 78.21 & \multirow{3}{*}{75.21} & \multirow{3}{*}{57.89} & \multirow{3}{*}{88.82} & \multirow{3}{*}{81.03} & \multirow{3}{*}{62.86} & \multirow{3}{*}{**98.67**} & \multirow{3}{*}{50.93} & \multirow{3}{*}{16.25} & \multirow{3}{*}{79.10} \\  & **Recall** & 80.29 & 70.12 & 75.21 & & & & & & & \\  & **F1 Sc. ** & 76.87 & 72.52 & & & & & & & & \\ \hline \multirow{3}{*}{ResNet + Grad-CAM} & **Prec. ** & 77.14 & 79.38 & \multirow{3}{*}{76.55} & \multirow{3}{*}{59.21} & \multirow{3}{*}{88.68} & \multirow{3}{*}{81.36} & \multirow{3}{*}{58.09} & \multirow{3}{*}{98.05} & \multirow{3}{*}{53.50} & \multirow{3}{*}{19.15} & \multirow{3}{*}{78.80} \\  & **Recall** & 80.91 & 72.19 & 76. As seen from Table 1, the fine-tuned ResNet-50 model achieved a precision of 76.64% for bipolar disorder and 79.60% for schizophrenia classification, with an accuracy of 76.77%. After that, we combine the features obtained from the fine-tuned ResNet-50 with Grad-CAM. Then, feature selection is applied to select the most important features. The selected features are used to classify bipolar disorder and schizophrenia. For this aim, first, SVM is employed for classification, and the model exhibited a lower overall accuracy of 75.19%. Secondly, we employed the XGBoost model in our problem, as given in Table 1. The model achieved an accuracy of 76.27%. After that, the AdaBoost and RF algorithms are utilized, and they yielded overall accuracies of 75.88% and 76.73%, respectively. Although RF achieved a higher overall accuracy than other models, the performance of all models is close to each other. The best classification performance is obtained by using the Bayesian-optimized DNN after employing feature selection. The accuracy of this model achieves a value of 78.84%. This model also outperforms other models, such as Support Vector Machines (SVM), AdaBoost, Random Forest (RF), and XGBoost in terms of all performance metrics. This indicates that Bayesian optimization effectively enhanced the model's accuracy and predictive capability, outperforming traditional machine learning algorithms. Furthermore, after performing feature fusion and feature selection processes, we achieved even more successful results compared to the ResNet model. The fusion and selection of features contributed significantly to enhancing model performance, underscoring the importance of dimensionality reduction techniques and optimal feature repre  
Figure 4: Original T1-weighted MR image, Grad-CAM heatmaps, the focused areas created from the heatmaps as contoured regions, corresponding binary masks, and masked areas on the T1 images of (**a**) SCH and (**b**) bipolar disorder. Figure 6 demonstrates the AUC and MCC performances for various machine learning and deep learning models. As shown in Figure 6a, ResNet + Grad-CAM + FS + BO-DNN outperformed other models with a mean AUC of 0.890, followed closely by the ResNet + Grad-CAM + FS + RF model with a mean AUC of 0.891.