## 6 Discussion
For example, LSVM and QSVM, attained 98.6% and 98.7% accuracy using combined Layer 1 features, 99.4% and 99.1% accuracy using combined Layer 2 features, and 94.7% and 94.7% accuracy using combined Layer 3 features in Scenario III. These accuracies were enhanced to 99.9% and 99.8% following FS in scenario IV. Likewise, CGSVM and CSVM enhanced from 98.7% for combined Layer 1 features, 99.5% for combined Layer 2 features, and (95.1%, 94.1%) for Layer 3 features to 99.8% and 99.8%, correspondingly. All classifiers exhibited perfect specificity and precision values of 1.000, with F1-scores reaching as high as 0.999 in scenario IV. Additionally, in contrast to the second scenario, LSVM enhanced from 99.1% (EfficientNetB0 Layer 2 with NNFM) to 99.9%, QSVM escalated from 98.3% (MobileNet Layer 1 with NNMF) to 99.8%, and CGSVM advanced from 98.0% (EfficientNetB0 Layer 1 features with NNMF) to 99.8%. Furthermore, in comparison to the initial scenario, LSVM enhanced from 99.4% (Layer 2 features of EfficientNetB0 of size 1280) to 99.9% after FS with only 400 features. MGSVM enhanced from 98.5% (Layer 1 features of MobileNet of size 1280) to 99.9% with 350 features, while QSVM progressed from 98.6% (Layer 1 features of EfficientNetB0 with size 980) to 99.8% with only 400 features. The enhancements were comparable in both CGSVM and CSVM, with accuracies increasing from 98.1% and 98.4% (Layer 1 features of MobileNet with size 980) to 99.8% and 99.8%, respectively with 400 features. These comparisons highlight the significant influence of mRMR FS in optimizing feature subsets to enhance classification accuracy while reducing the cost of computation. The proposed CAD system achieved state-of-the-art accuracy by successfully combining and optimizing features from various CNN layers and structures, highlighting its capacity as a dependable and effective approach for non-invasive breast cancer detection, especially in resource-constrained environments. Although feature extraction is routinely automated by deep networks using hierarchical learning, CNN layers encompass different levels of abstraction, ranging from low-level textures in the first layers to high-level meaningful patterns in the deeper layers. Exclusively depending on the terminal layers of a singular network may neglect intricate, layer-specific characteristics, especially in thermal imaging where pathological signs appear as localised thermal disparities instead of structural anomalies. The proposed framework utilizes features extracted from various layers of three CNN topologies (MobileNet, EfficientNetB0, and ShuffleNet) to capitalize on the unique advantages of different representations. Initial layers preserve nuanced thermal variations essential for identifying subtle anomalies, whereas intermediate layers capture spatial dependencies that augment discriminative capability. End-to-end deep learning classification, although potent, exhibits specific drawbacks, especially in the context of medical imaging tasks. These models frequently necessitate substantial training data and computational power, and they may experience overfitting, particularly when confronted with small or imbalanced datasets, which is prevalent in medical diagnostics. Therefore, the dataset employed in the present research is insufficient for comprehensive end-to-end classification. The direct utilization of the final layers for classification may overlook significant intermediate features, thereby constraining the model's capacity to discern nuanced patterns in thermal images. To mitigate these limitations, the study utilized a transfer learning-based feature extraction method in conjunction with conventional classifiers. Transfer learning enables the use of pre-trained CNN algorithms, minimizing the requirement for extensive datasets and expediting convergence. The proposed framework obtained multi-layer features instead of depending exclusively on the networks' final layers, which were subsequently enhanced with DWT and NNMF to minimize noise and redundancy.