## 4 Experimental Results and Analysis
### Model Performance  
In this paper, an adaptive multimodal and multiscale feature fusion classification network is proposed. Under the condition of considering multimodal information and multiscale information at the same time, the accurate prediction of tumor classification can be achieved without sketching the lesion area. As shown in Figure 9, its AUC can reach 0.965, which is higher than the existing mainstream deep learning classification model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & _p1\%_ & **R/\%** & **F1-Measure/\%** \\ \hline AMMFNet + SE & 77.66 & 77.36 & 77.51 \\ IoMET & 61.46 & 61.10 & 61.28 \\ Bi\_lstm & 76.42 & 74.83 & 75.65 \\ Bi\_GRU & 75.03 & 74.61 & 74.82 \\ GRNN & 74.71 & 75.67 & 75.19 \\ BERT & 78.38 & 78.32 & 78.35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results of different classification models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & \
\begin{tabular}{c} **Positive** \\ **Predictive Rate** \\ \end{tabular} &
\begin{tabular}{c} **Negative** \\ **Predictive Rate** \\ \end{tabular} & **Average F1** \\ \hline AMMFNet & 0.913 & 0.928 & 0.88 & 0.946 & 0.844 & 0.916 \\ \hline AMMFNet + Res & 0.894 & 0.893 & 0.897 & 0.951 & 0.788 & 0.908 \\ \hline AMMFNet + Res + SE & 0.906 & 0.937 & 0.837 & 0.928 & 0.855 & 0.901 \\ \hline AMMFNet + SE & 0.922 & 0.929 & 0.905 & 0.956 & 0.850 & 0.928 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of evaluation parameters of different optimization models on tumor grading.  
When using the focus area prediction, its AUC is as high as 0.997, which is better than the latest model of tumor grading.  
As shown in Figure 10, the multimodal data can complement one another, and have a richer feature search space than the unimodal data, while the multimodal feature extraction module designed in this paper can effectively explore the semantic features useful for classification. The adaptive multimodal multiscale feature fusion module can increase the adaptability of the fusion rules to a certain extent by using the learnable convolutional approximation fusion rules. However, in practice, there are still some open issues in the experimental process. For example, although our method can classify tumors by the differences in their multimodal features, it is difficult to further improve the classification accuracy due to the inconsistent training speed of multiple modalities during training. In addition, among the four modalities, the tumor area was not effectively identified from the T1 and Flair modal images, and their inclusion in the multimodal model would reduce the prediction accuracy of the model. This suggests that while using the multimodal adaptive fusion strategy, stronger constraints should be added to each modal datum in order to ensure that the training speed of each modal feature extraction module is essentially the same, so as to reduce the influence of the multimodal model on individual modalities. From the results of the model optimization analysis, it is clear that the introduction of different submodules requires consideration of the specific problem to be solved by the network. In addition to the introduction of the SE module, the introduction of a newer image attention mechanism in the learning module of the adaptive fusion rules is also one of the options for future optimization. In addition to the optimization of the fusion module, the depth of the network model designed in this paper is only 11 layers, and the network can be deepened. The deeper the network is, the more semantic it is, and the better it can be expressed. However, as the depth of the network increases, the number of network parameters gradually increases, and it is also worth exploring how to improve the hierarchical performance of the model by reducing the network parameters while deepening the network based on the design ideas of frontier networks.