## 2 Materials and Methods
### Model Training and Evaluation  
In this study, the model's generalization ability was evaluated by dividing the dataset into training, validation, and test sets in a 6:2:2 ratio. The dataset underwent random partitioning to ensure unbiased evaluation. Subsequently, the evaluation metrics were calculated based on the average results from 50 tests, providing robust insights into the model's performance under diverse conditions.  
These evaluation metrics, including classification accuracy, Area Under the Curve (AUC), sensitivity, specificity, recall rate, and F1 score, were utilized to comprehensively assess the model's performance. Each metric offers unique insights into different aspects of the model's predictive capabilities. For instance, accuracy measures overall correctness in predictions, while precision and recall focus on correctly identifying positive instances. Specificity evaluates the model's ability to identify negative instances accurately, and the F1 score provides a balanced measure of precision and recall. The calculations of these metrics were based on Equations (1)-(5), where TP, FP, TN, and FN represent true positive, false positive, true negative, and false negative cases, respectively. The performance indicators are represented as the mean value plus or minus the standard deviation from these tests.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{1}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{F1 Score }=2\times\frac{\text{Precision}\times\text{Sensitivity}}{\text{ Precision}+\text{Sensitivity}} \tag{5}\]  
In our study, we conducted a two-part experimental investigation to assess the impact of attention modules and CentralNet on MRI data classification. The first part focused on single modality experiments, where the model was individually fed with either T1 or T2 modality images. The primary objective here was to compare the classification performance of models without the attention module against those equipped with it. This comparison aimed to elucidate the specific contribution of an AM in enhancing the model's ability to classify MRI images based on single modalities.  
Following the single modality experiments, we delved into multi-modal classification. Initially, we adopted the early fusion method as described in [PERSON] et al.'s study [22], where T1 and T2 MRI modalities were concatenated at the outset before being input into the model. This approach set the baseline for our multi-modal analysis. Subsequently, we implemented a later fusion strategy, differing from early fusion by merging features from T1 and T2 modalities at a later stage in the model's processing. This step was crucial in understanding the benefits of fusing features at different stages of the model. The experiments were further extended by integrating attention modules into the later fusion framework. Finally, our proposed model, incorporating both an AM and CentralNet, was evaluated to determine its efficacy in optimizing classification performance in a multi-modal MRI context.  
To achieve effective model parameter initialization and improved training efficiency, we utilized transfer learning techniques. The pre-trained weights from ImageNet were downloaded from the PyTorch website ([[https://download.pytorch.org/models/resnet18-5c106](https://download.pytorch.org/models/resnet18-5c106) cde.pth]([https://download.pytorch.org/models/resnet18-5c106](https://download.pytorch.org/models/resnet18-5c106) cde.pth), accessed on 7 December 2023) to initialize the weights of the feature extraction module in our model. The hyperparameters for training were set as follows: an initial learning rate of 0.0001, maximum epoch of 200, and a minibatch size of 32. The Adam algorithm was used to train the model. This experiment was conducted using Python 3.10 on a Windows 11 operating system.