## 4 Experiment and Results
### Experiment Results and Analysis
#### 4.4.3 Model Evaluation
Among the top 15 features across all four ensemble models, deep features consistently accounted for at least one-third of the features. In Groups C and D, deep features even ranked among the top three. This highlights the significant role that deep features play in the composite decision-making process of the model post-fusion. Further, in the individual classifier feature weight plots in Appendix A, deep features also consistently exhibit significant weight. These results confirm that deep features provide reliable and consistent information for classification. Their interpretability lies in the fact that these features capture patterns similar to those of \"clinical features,\" such as complex textures and abstract semantic information in the image data. These patterns contribute substantially to the classification performance across different model frameworks. To further explore the correspondence between the deep feature extraction results and clinical indicators, and thereby enhance the interpretability of the model, we computed the NMI values for two sets of deep features and radiomics features, as shown in Figure 8. We found that the seventh radiomics feature (original_shape2D_MinorAxisLength) stands out with a significantly higher NMI value compared to the other features, suggesting that there is shared information between the deep features and those with actual physical \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Structure**} & \multirow{2}{*}{**Source/Year**} & \multicolumn{3}{c}{**Evaluation Metrics**} \\  & & **(LIDC-IDRI)** & \\ \cline{3-5}  & & **ACC** & **SPE** & **SEN** \\ \hline LSTM + CNN & Biomedical Signal Processing and & & & \\ + Multisemantic Features [53] & Control/2023 & 0.95 & 0.93 & 1.0 \\ \hline CNN+ATSO [54] & Journal of Digital Imaging/2023 & 0.96 & / & 0.94 \\ \hline Radiomics + CNN [55] & Insights Imaging/2023 & 0.90 & 0.94 & 0.90 \\ \hline Ensemble DLM [56] & Neural Computing and & & & \\ Applications/2023 & 0.97 & / & 0.98 \\ \hline DDDG-GAN [57] & BMC Med Inform Decis Mak/2024 & 0.93 & / & / \\ \hline Fusion model [58] & BMC Medical Imaging/2024 & 0.94 & / & 1 \\ \hline MVSA-CNN [59] & Diagnostics(Basel)/2024 & 0.97 & 0.96 & 0.97 \\ \hline Group A & & 0.94 & 0.88 & 0.97 \\ Group B & & 0.98 & 0.96 & 0.98 \\ Group C & & 0.95 & 0.93 & 0.96 \\ Group D & & 0.98 & 0.96 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._characteristics. In addition, several other feature pairs also exhibit notable associations, indicating potential underlying relationships. This finding provides valuable insights for interpreting deep features and enhances the model's interpretability. It should be noted that as the number of features increases, the relationships between features become sparser and more complex, which means that even if certain features show statistically significant correlations, their mutual information values may still be relatively low. The normalization process ensures that this comparison is fair, and from the results, we can conclude that this relationship is not due to noise or random coincidence. Figure 8: Normalized mutual information heatmap. Figure 7: Feature weight map of the integrated model ensemble.