## 3 Results
### Validation of the Effectiveness of the Proposed HSSFM  
The previous ablation experiment demonstrated the advantage of the HSSFM in integrating hierarchical spectral semantic information. To further evaluate the effectiveness and generalization capability of hierarchical spectral semantic modeling for the classification of the HSSFM, we conducted comparative experiments on several state-of-the-art deep learning models, including ResNeXt, MobileNetV3-s, SENet, ResNet50, and ResNet101. The use of a single-level feature refers to directly utilizing the final feature layer of different models for classification after pooling or flattening. In contrast, a hierarchical feature extracts multi-scale feature layers from the model and employs the HSSFM to integrate hierarchical spectral semantics, generating more discriminative features for classification.  
The experimental results (Table 8) demonstrate that the HSSFM effectively enhances classification performance across all network architectures. Compared to methods that rely solely on the final feature layer for classification, the HSSFM integrates multi-scale spectral semantic information, achieving superior performance in terms of accuracy, precision, recall, and \(F1\)-_score_. Notably, in deeper networks such as ResNet50 and ResNet101, the HSSFM improves classification accuracy by 5.1% and 5.0%, respectively. This improvement can be attributed to the more structured hierarchical features in deeper networks, allowing the HSSFM to more effectively capture and integrate multi-scale spectral features, thereby enhancing the model's discriminative capability.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Fusion Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{ShuffleNetV2} & Data Fusion & 88.2\% & 88.4\% & 88.6\% & 88.5\% \\  & MSFM & **89.9\%**\(\uparrow\) 1.7\% & 90.3\% & 90.3\% & 90.3\% \\  & Data Fusion & 88.1\% & 88.3\% & 88.5\% & 88.4\% \\  & MSFM & **92.0\%**\(\uparrow\) 3.9\% & 92.3\% & 92.1\% & 92.2\% \\ \multirow{2}{*}{MobileNetV3s} & Data Fusion & 86.6\% & 87.2\% & 87.0\% & 87.1\% \\  & MSFM & **89.8\%**\(\uparrow\) 3.2\% & 90.4\% & 90.2\% & 90.3\% \\ \multirow{2}{*}{SENet} & Data Fusion & 91.5\% & 91.1\% & 91.7\% & 91.4\% \\  & MSFM & **93.1\%**\(\uparrow\) 2.4\% & 92.7\% & 93.5\% & 93.1\% \\ \multirow{2}{*}{ResNet50} & Data Fusion & 88.7\% & 88.6\% & 89.0\% & 88.8\% \\  & MSFM & **93.0\%**\(\uparrow\) 4.3\% & 93.1\% & 93.3\% & 93.2\% \\ \multirow{2}{*}{ResNet101} & Data Fusion & 89.2\% & 88.8\% & 89.4\% & 89.1\% \\  & MSFM & **92.4\%**\(\uparrow\) 3.2\% & 92.3\% & 92.7\% & 92.5\% \\ \hline \hline \end{tabular}  
* **Note:** Bold values indicate the best performance achieved by the MSFM compared to the baseline Data Fusion method. The \(\uparrow\) symbol denotes the improvement in percentage points over the baseline.  
\end{table}
Table 7: Generalization validation results of the MSFM across different networks.  
Overall, this experiment demonstrates the effectiveness and generalization capability of the HSSFM in multi-scale feature fusion. By deeply integrating hierarchical semantic information, the HSSFM enhances the model's ability to identify diseased regions, further highlighting the significance of hierarchical spectral information in disease classification tasks.