## 3 Results

Table 2: Performance evaluation of single-modal MRI data with and without attention module across various metrics. Following early fusion, we explored the \"None (Later Fusion)\" model, wherein the T1 and T2 features were combined at a later stage in the model's processing. This later fusion strategy outperformed early fusion, achieving an accuracy of 0.728 \(\pm\) 0.054, a sensitivity of 0.747 \(\pm\) 0.103, and a specificity of 0.708 \(\pm\) 0.035. Integrating attention modules (AMs) into the later fusion framework further improved the performance, resulting in the \"AM (Later Fusion)\" model, which attained an accuracy of 0.766 \(\pm\) 0.013 and a sensitivity of 0.795 \(\pm\) 0.055. The most significant advancements, however, were observed with the \"AM + CentralNet\" model, where attention modules were combined with CentralNet for feature fusion. This approach achieved the highest performance, with an accuracy of 0.845 \(\pm\) 0.018 and sensitivity of 0.954 \(\pm\) 0.069, underscoring the effectiveness of attention mechanisms and advanced fusion techniques in enhancing multi-modal MRI classification. Figure 5 presents the performance metrics of different models for ABE classification. Panel (a) shows the confusion matrix for our proposed model, illustrating the number of true positives (ABE correctly identified as ABE) at 26 and true negatives (non-ABE correctly identified as non-ABE) at 23, indicating a strong predictive capability. However, there are instances of misclassification, with 7 false negatives (ABE incorrectly identified as non-ABE) and 1 false positive (non-ABE incorrectly identified as ABE), which suggests areas for potential improvement in the model's sensitivity. In our study, we meticulously evaluated the processing times across different computational models during various stages of our experiment, including preprocessing, training, and testing (Table 4). The preprocessing stage displayed a uniform duration across all the  
Figure 5: Performance metrics of different models for ABE classification. (**a**) Confusion matrix of our model; (**b**) Comparison of ROC curves for different models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Module** & **MRI Modality** & **Accuracy** & **AUC** & **Sensitivity** & **Specificity** & **Precision Score** & **F1 Score** \\ \hline None (Early Fusion) & T1 + T2 & 0.648 \(\pm\) 0.043 & 0.693 \(\pm\) 0.039 & 0.709 \(\pm\) 0.078 & 0.586 \(\pm\) 0.048 & 0.639 \(\pm\) 0.033 & 0.671 \(\pm\) 0.048 \\ AM (Early Fusion) & T1 + T2 & 0.663 \(\pm\) 0.046 & 0.738 \(\pm\) 0.034 & 0.714 \(\pm\) 0.103 & 0.611 \(\pm\) 0.062 & 0.655 \(\pm\) 0.034 & 0.68 \(\pm\) 0.056 \\ None (Later Fusion) & T1 + T2 & 0.728 \(\pm\) 0.054 & 0.77 \(\pm\) 0.024 & 0.747 \(\pm\) 0.103 & 0.708 \(\pm\) 0.035 & 0.724 \(\pm\) 0.038 & 0.733 \(\pm\) 0.067 \\ AM (Later Fusion) & T1 + T2 & 0.766 \(\pm\) 0.013 & 0.804 \(\pm\) 0.027 & 0.795 \(\pm\) 0.055 & 0.735 \(\pm\) 0.059 & 0.759 \(\pm\) 0.027 & 0.775 \(\pm\) 0.017 \\ AM + CentralNet & T1 + T2 & 0.845 \(\pm\) 0.018 & 0.913 \(\pm\) 0.02 & 0.954 \(\pm\) 0.069 & 0.732 \(\pm\) 0.077 & 0.792 \(\pm\) 0.048 & 0.862 \(\pm\) 0.017 \\ \hline \hline \end{tabular}  
* AM = attention module; None = model without attention module; fusion strategy: early fusion, later fusion, CentralNet.  
\end{table} 
Table 3: Impact of attention modules and CentralNet on classification metrics using combined T1 and T2 MRI modalities. models, with each taking approximately 59 s per sample. During the training and testing phases, we noted significant differences in the processing times depending on whether an attention module was used and the type of fusion strategy employed. The 'None' model, which did not include an attention module, exhibited the shortest processing times. Conversely, models using either early fusion or later fusion strategies, especially those incorporating an attention module, required longer processing times due to the increased computational complexity.