## 6 Discussion  
The present study introduced a CAD paradigm that integrates thermal imaging, compact CNN structures, sophisticated feature transformation, selection, and combination methods to improve breast cancer identification. The CAD underwent assessment in four classification scenarios, providing a thorough understanding of the roles of individual layers, dimensionality reduction, multi-CNN feature integration, and FS in enhancing diagnostic performance. In the initial scenario, to evaluate each layer's impact on identification, attributes were taken out of three different layers namely Layer 1, Layer 2, and Layer 3 of MobileNet, EfficientNetB0, and ShuffleNet and used to train the SVM classifiers independently. EfficientNetB0 proved to be the most proficient CNN, with Layer 2 attaining a peak accuracy of 99.4% across LSVM, QSVM, and CSVM classifiers. MobileNet demonstrated commendable performance, achieving 98.6% accuracy with its Layer 1 features when employing QSVM. In contrast, ShuffleNet exhibited inferior solo performance, reaching a maximum of 97.2% in Layer 2 with MGSVM. In addition, Layer 3 attributes across all CNNs demonstrated the poorest performance, with accuracies falling below 91.4%, highlighting their restricted discriminative capability when deployed independently. On the other hand, the second scenario examined the effects of dimensionality reduction via NNMF on variables derived from Layers 1 and 2. The identification accuracy was enhanced while preserving computational efficiency by lowering feature dimensions to 40-80 after applying NNMF. Layer 2 attributes of EfficientNetB0 attained 99.1% accuracy with LSVM leveraging 80 features, demonstrating NNMF's potential to maintain diagnostic significance while reducing sophistication. This dimensionality reduction efficiently balanced accuracy and feature length. Layer 2 variables significantly surpassed Layer 1 throughout all deep learning structures, highlighting their significance in classification tasks. QSVM and MGSVM proved to be highly efficient classifiers, effectively using features, while the selection of appropriate feature dimensionality (40-60 for Layer 1, 60-100 for Layer 2) was essential to prevent redundancy and uphold high accuracy. These results highlight the necessity of rigorously choosing CNN layers, SVM classifiers, and feature subset lengths to optimize diagnostic accuracy. EfficientNetB0, utilizing Layer 2 features and LSVM, constitutes the most advantageous architecture for effective breast cancer identification systems. The third scenario emphasized the combination of features throughout the CNNs for each layer. The fusion of attributes markedly improved classification accuracy, particularly for Layer 3, where the combination of features elevated accuracy to 95.1%, in contrast to independent results that fell below 91.4%. The integration of Layer 2 features attained an accuracy of 99.8% with MGSVM, illustrating that the integration of complementary features from various CNNs encompasses diverse representations and enhances diagnostic performance. The results from Table 5 highlighted the substantial influence of feature combinations on improving classification accuracy. Prior to integration, each CNN offeredrestricted but complementary perspectives. Through the integration of features, especially for Layer 3, the system attained a cohesive representation that capitalized on the distinct advantages of each CNN, resulting in significant performance improvements. This illustrates the essential function of feature integration in enhancing the resilience and efficacy of CAD systems for breast cancer classification. Finally, the fourth scenario employed mRMR FS on the integrated multi-layer attributes, attaining an outstanding accuracy of 99.9% with MGSVM exploiting merely 350 attributes. This indicated a significant enhancement across all classifiers relative to prior scenarios.