## 5 Results
### Parameter Analysis  
#### 5.2.1 Reduction Dimensionality  
This subsection examines the impact of dimensionality reduction through NNMF on classification efficacy. This study investigates the impact of altering the quantity of selected features (ranging from 10 to 100) derived from Layer 1 and Layer 2 of each CNN on the accuracy of various SVM classifiers. Tables 3 and 4 provide an extensive overview of the identification accuracy derived from attributes gathered from Layer 1 and Layer 2 of EfficientNetB0, MobileNet, and ShuffleNet, subsequent to dimensionality reduction through NNMF. The results provide essential insights into the impact of various layers and SVM classifiers on the efficacy of breast cancer classification employing thermal imaging. Table 3 illustrates that the accuracy of the three deep neural networks leveraging features from Layer 1 varies according to the dimension of the feature set and the SVM classifier employed. The maximum classification accuracy for EfficientNetB0 was 98.1%, attained with both MGSVM and CGSVM exploiting feature sets of 10 and 80 dimensions, correspondingly. The comparable efficiency across various feature set lengths emphasizes the value of Layer 1 attributes in EfficientNetB0 for classification tasks, likely attributable to their capacity to collect lower-level spatial information. MobileNet's Layer 1 features achieved a peak accuracy of 98.6% with QSVM employing 40 features This outcome indicates that the features from Layer 1 of MobileNet proficiently encapsulate essential patterns for classification, leveraging concise yet meaningful representations. Nevertheless, accuracies diminished marginally as the feature set size exceeded 40, suggesting that excessive representation may introduce redundancy, thereby diminishing classifier efficacy. ShuffleNet demonstrated relatively lower accuracy, attaining a maximum performance of 96.6% when utilizing QSVM and MGSVM with 90 features. Notwithstanding the enhancements noted with increased feature dimensions, ShuffleNet's performance remained inferior to that of EfficientNetB0 and MobileNet, indicating that its \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Model** & **CNN Layer** & **LSVM** & **QSVM** & **CSVM** & **MGSVM** & **CGSVM** \\ \hline \multirow{3}{*}{**MobileNet**} & Layer 1 & 98.2 & 98.2 & 98.4 & 98.5 & 98.1 \\  & Layer 2 & 97.2 & 96.9 & 96.6 & 97.2 & 96.1 \\  & Layer 3 & 91.4 & 91.4 & 73.5 & 90.1 & 90.2 \\ \hline \multirow{3}{*}{**EfficientNetB0**} & Layer 1 & 98.2 & 98.6 & 98.6 & 98.4 & 98.0 \\  & Layer 2 & 99.4 & 99.4 & 99.4 & 99.3 & 99.2 \\  & Layer 3 & 88.7 & 89.3 & 88.5 & 89.0 & 88.5 \\ \hline \multirow{3}{*}{**ShuffleNet**} & Layer 1 & 95.6 & 96.1 & 96.3 & 96.4 & 92.8 \\  & Layer 2 & 96.0 & 96.5 & 96.5 & 97.2 & 93.6 \\ \cline{1-1}  & Layer 3 & 90.6 & 85.9 & 37.4 & 89.3 & 89.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An ablation study showing the identification accuracy (%) achieved by the SVM classifiers trained with deep attributes acquired from different layers of each deep neural network. Layer 1 features may be deficient in robustness and discriminative capability compared to the other topologies. \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  & 10 & 97.2 & 97.8 & 97.6 & 98.1 & 96.2 \\  & 20 & 98.6 & 98.7 & 98.7 & 98.8 & 98.5 \\  & 30 & 98.7 & 99.0 & 99.0 & 98.4 & 98.1 \\  & 40 & 98.7 & 98.4 & 98.5 & 98.5 & 98.6 \\
**EfficientNetB0** & 50 & 98.9 & 99.1 & 99.0 & 99.0 & 98.3 \\
**Layer 2 Deep** & 60 & 98.5 & 98.2 & 98.0 & 98.5 & 98.6 \\
**Features** & 70 & 98.7 & 99.0 & 98.7 & 98.9 & 98.7 \\  & 80 & 99.1 & 98.8 & 98.8 & 98.6 & 98.8 \\  & 90 & 99.2 & 99.1 & 98.9 & 98.8 & 98.8 \\  & 100 & 98.5 & 98.8 & 98.7 & 98.2 & 98.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The identification accuracy (%) attained using the SVM classifiers learned with the reduced NNMF variables obtained from layer 2 of each deep neural network. \begin{table}