## 5 Results
### Parameter Analysis
#### 5.2.2 Feature Fusion  
This subsection examines the advantages of integrating deep features from various CNN architectures (MobileNet, EfficientNetB0, and ShuffleNet) at the layer level. It examines the influence of fusing each layer of the three CNNs on the classification performance. Table 5 illustrates the influence of feature combination on classification efficacy independently throughout Layers 1, 2, and 3 of MobileNet, EfficientNetB0, and ShuffleNet. The combination of attributes from the different CNNs generally enhanced diagnostic performance, with the greatest pronounced improvements noted in Layer 3. Prior feature  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Model and** **Layer** & **Number of Features** & **LSVM** & **QSVM** & **CSVM** & **MGSVM** & **CGSVM** \\ \hline \multirow{8}{*}{**MobileNet Layer**} & 10 & 96.4 & 97.2 & 97.5 & 97.2 & 94.7 \\  & 20 & 97.0 & 97.2 & 97.4 & 97.5 & 96.9 \\  & 30 & 97.0 & 97.2 & 97.4 & 97.5 & 96.9 \\  & 40 & 97.4 & 97.4 & 97.3 & 97.3 & 97.4 \\
**MobileNet Layer** & 50 & 97.1 & 97.2 & 97.0 & 97.3 & 97.3 \\
**2 Deep Features** & 60 & 97.1 & 96.9 & 96.7 & 96.9 & 97.1 \\  & 70 & 97.5 & 96.9 & 97.0 & 97.0 & 97.2 \\  & 80 & 97.2 & 97.6 & 97.3 & 97.5 & 97.2 \\  & 90 & 97.3 & 96.9 & 97.5 & 97.1 & 97.7 \\  & 100 & 97.6 & 97.5 & 97.6 & 97.2 & 97.0 \\ \hline \multirow{8}{*}{**ShuffleNet Layer**} & 10 & 93.9 & 96.2 & 96.2 & 96.8 & 93.0 \\  & 20 & 95.1 & 97.0 & 96.5 & 96.2 & 93.9 \\  & 30 & 96.3 & 97.0 & 96.4 & 96.2 & 95.5 \\  & 40 & 96.0 & 95.5 & 95.5 & 96.4 & 95.8 \\  & 50 & 96.4 & 96.5 & 96.2 & 96.3 & 96.1 \\
**2 Deep Features** & 60 & 96.7 & 96.0 & 96.0 & 96.4 & 95.5 \\  & 70 & 96.1 & 95.8 & 95.8 & 95.9 & 95.9 \\  & 80 & 96.7 & 97.0 & 96.8 & 96.9 & 95.8 \\  & 90 & 96.4 & 96.8 & 96.1 & 96.4 & 96.1 \\  & 100 & 97.2 & 96.7 & 95.7 & 96.5 & 96.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._combination, MobileNet's highest accuracy for Layer 1 was 98.5% with MGSVM, EfficientNetB0's was 98.6% with QSVM and CGSVM, and ShuffleNet's was 96.4% with MGSVM. The integration of features from all three CNNs enhanced accuracy to 98.8% with MGSVM and 98.7% with other classifiers except for LSVM which reached 98.6%, indicating a slight yet significant improvement. This enhancement illustrates that feature incorporation successfully integrates the advantages of distinct layouts, enhancing the lower-level spatial data acquired in Layer 1. On the other hand, in Layer 2, EfficientNetB0 surpassed each of the single CNNs, attaining 99.4% accuracy with LSVM, QSVM, and CSVM prior to feature combination, whereas MobileNet and ShuffleNet reached maximum accuracies of 97.2% with LSVM and MGSVM, correspondingly. Following feature combination, the accuracy rate increased to an exceptional 99.8% with MGSVM, while other classifiers consistently attained high results of 99.5% except for LSVM (99.4%). This improvement emphasizes the essential function of incorporating intermediate-layer features, which encapsulate a balance of spatial and semantic information, to optimize the accuracy of classification. Whereas in Layer 3, independent findings were significantly inferior for each of the CNNs. MobileNet attained a maximum accuracy of 91.4% using LSVM and QSVM, EfficientNetB0 attained 89.3% with QSVM, and ShuffleNet scored 90.6% with LSVM. Following the feature combination, the accuracy increased to 95.1% with CSVM, 94.7% for LSVM and QSVM, and 94.1% for MGSVM and CGSVM indicating the most significant enhancement across all layers. This substantial improvement highlights the efficacy of feature combinations in offsetting the deficiencies of deeper layers, where independent CNNs could fall short of delivering adequate discriminative capability.