## 5 Results
### Parameter Analysis
#### 5.2.3 Feature Selection
This suggests that although diminishing feature dimensions can substantially decrease computational demands, it must be executed carefully to retain the most informative features that significantly aid classification. The study utilizes the MRMR-based FS method to attain an optimal equilibrium between performance and computational efficiency, prioritizing features according to their relevance to the target class while reducing redundancy. This guarantees that, despite a diminished feature set, the chosen attributes are optimally informative and minimally redundant. Table 6 illustrates that when the feature count attains 350, the classification accuracy stabilises at 99.9%, signifying that additional increments in feature count do not produce substantial enhancements in performance. Consequently, choosing approximately 350 features achieves a pragmatic balance--maintaining elevated diagnostic precision while significantly reducing memory consumption, training duration, and inference complexity relative to employing the complete feature set, which initially encompassed over 1000 dimensions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **LSVM** & **QSVM** & **CSVM** & **MGSVM** & **CGSVM** \\ \hline
50 & 96.9 & 97.7 & 98.4 & 96.8 & 96.1 \\
100 & 97.8 & 98.9 & 98.9 & 97.9 & 97.2 \\
150 & 98.6 & 99.3 & 99.4 & 99.0 & 97.8 \\
200 & 99.4 & 99.4 & 99.5 & 99.5 & 98.2 \\
250 & 99.4 & 99.6 & 99.5 & 99.5 & 98.8 \\
300 & 99.4 & 99.6 & 99.6 & 99.5 & 99.4 \\
350 & 99.6 & 99.7 & 99.7 & 99.9 & 99.6 \\
400 & 99.9 & 99.8 & 99.8 & 99.9 & 99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The identification accuracy (%) reached by the SVM classifiers versus the number of features selected using the MRMR FS approach and fed to these classifiers. This optimized feature subset improves generalization by reducing overfitting risk and enables deployment in resource-limited settings like rural clinics or mobile health units. Consequently, the incorporation of MRMR-based feature selection allows the proposed CAD system to sustain outstanding diagnostic efficacy while guaranteeing scalability and efficiency in various clinical environments. More evaluation measures are also calculated for the SVM classifiers trained with 350 (for MGSVM) and 400 features (all other SVMs) selected after the MRMR FS procedure. Table 7 shows these performance metrics. The outcomes of Table 7 exhibit outstanding consistency and nearly flawless performance throughout different metrics for assessment. Sensitivity scores span from 0.9960 to 0.9980, demonstrating an exceptional capacity to accurately detect positive breast cancer instances with negligible false-negative rates. Specificity and precision indicators consistently attain a maximum value of 1.000, indicating flawless differentiation between normal and abnormal thermal images with no false-positive occurrences. The F1-scores, varying from 0.9980 to 0.9990, highlight the remarkable equilibrium between precision and recall. The MCC, ranging from 0.9960 to 0.9980, reinforces the strong discriminative capability of the presented CAD system. LSVM and MGSVM slightly surpass other variants, attaining the highest sensitivity and F1-score of 0.9980, indicating their potential superiority in classifying breast cancer thermal images. The findings illustrate the effectiveness of the suggested multi-layer feature extraction, dimensionality reduction, and FS procedure in creating a highly accurate breast cancer diagnostic framework. The confusion matrices illustrated in Figure 3 confirm the exceptional efficacy of the LSVM and MGSVM classification algorithms, showcasing their capacity to accurately categorize almost all instances as either normal or abnormal with only one instance that has been misclassified. This validates the classifiers' capacity to consistently differentiate among normal and abnormal breast thermal photos, an essential criterion for early cancer detection and prevention.