## 5 Results
### Parameter Analysis
#### 5.2.3 Feature Selection  
This subsection delineates the outcomes of implementing MRMR-based feature selection on the integrated multi-layer features. It examines the effect of augmenting the number of selected features (from 50 to 400) on classification accuracy. A thorough examination of Table 6 shows the intricate connection between the classification accuracy of various SVM versions and the amount of attributes chosen using MRMR. The results indicate a steady pattern of enhanced classification accuracy as the number of attributes rises from 50 to 400. First, using 50 features, the identification accuracy scores vary from 96.1% (CGSVM) to 98.4% (CSVM), reflecting average performance. A substantial rise in accuracy is seen for all SVM classifiers as the attribute size steadily rises. The performance is especially notable when 350-400 features are chosen, with classification accuracies nearing perfection, achieving up to 99.9% for MGSVM and LSVM at 350 and 400 variables, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Layer** & **CNN Model** & **LSVM** & **QSVM** & **CSVM** & **MGSVM** & **CGSVM** \\ \hline \multirow{3}{*}{**Layer 1**} & **MobileNet** & 98.2 & 98.2 & 98.4 & 98.5 & 98.1 \\  & **EfficientNet** & 98.2 & 98.6 & 98.6 & 98.4 & 98.0 \\  & **SuffleNet** & 95.6 & 96.1 & 96.3 & 96.4 & 92.8 \\  & **Combined Features** & 98.6 & 98.7 & 98.7 & 98.8 & 98.7 \\ \hline \multirow{3}{*}{**Layer 2**} & **MobileNet** & 97.2 & 96.9 & 96.6 & 97.2 & 96.1 \\  & **EfficientNet** & 99.4 & 99.4 & 99.4 & 99.3 & 99.2 \\  & **ShuffleNet** & 96.0 & 96.5 & 96.5 & 97.2 & 93.6 \\  & **Combined Features** & 99.4 & 99.5 & 99.5 & 99.8 & 99.5 \\ \hline \multirow{3}{*}{**Layer 3**} & **MobileNet** & 91.4 & 91.4 & 73.5 & 90.1 & 90.2 \\  & **EfficientNet** & 88.7 & 89.3 & 88.5 & 89.0 & 88.5 \\ \cline{1-1}  & **SuffleNet** & 90.6 & 85.9 & 37.4 & 89.3 & 89.1 \\ \cline{1-1}  & **Combined Features** & 94.7 & 94.7 & 95.1 & 94.1 & 94.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The identification accuracy (%) accomplished with the SVM classifiers learned with the combined deep feature sets of each independent layer (layer 1, layer 2, and layer 3) of the three CNNs. In addition, the QSVM and CSVM demonstrate exceptional performance, consistently achieving accuracies reaching greater than 99.0% with the inclusion of over 150 features. This consistent improvement indicates an effective FS strategy, wherein additional feature dimensionality markedly improves diagnostic accuracy, though diminishing returns are noted beyond 350 features. Furthermore, QSVM, CSVM, and CGSVM reached 99.8% with 400 features. These features are much lower with higher performance (Table 2) than those of Layer 1 and Layer 2 features in scenario I of the proposed CAD. Moreover, these results are higher than those obtained in scenarios II and III of the proposed CAD (Tables 3-5). The results highlight the essential need for meticulous FS and dimensionality reduction in creating effective CAD systems for breast cancer detection via thermal imaging, indicating that deliberate FS can significantly enhance classification efficacy. The results of Table 6 demonstrate that a reduction in the number of selected features correlates with a decrease in classification accuracy. This trend highlights the nuanced equilibrium between computational effectiveness and model accuracy in the suggested CAD system. With lower feature counts, specifically 50 or 100, the SVM classifiers demonstrate diminished accuracy--between 96.1% and 98.4%--indicating that excessive reduction in feature dimensionality may deprive the model of essential discriminative information required for precise breast cancer detection. Nonetheless, as the quantity of selected features surpasses 150, the classification performance consistently enhances, attaining outstanding accuracy (99.9%) at 350-400 features.