## 6 Discussion
This optimized feature set was later categorized using SVM. By separating feature extraction from classification, the study enhanced accuracy and markedly decreased computational complexity, thereby rendering the model more appropriate for real-time and resource-limited contexts. Furthermore, the effective use of DWT and NNMF fulfils two functions. Thermal images frequently exhibit noise resulting from physiological fluctuation and environmental influences. DWT disaggregates attributes into frequency sub-bands, separating physiologically pertinent thermal patterns while attenuating noise. NNMF mitigates redundancy by mapping attributes into a lower-dimensional subspace that retains non-negative, interpretable components, a characteristic especially beneficial for medical diagnostics. These transformations are not simply re-analyses of pre-extracted attributes but deliberate measures to enhance and streamline information, guaranteeing computational efficiency while maintaining diagnostic accuracy. In addition, the MRMR feature selection technique determines a concise subset of attributes that maximizes discriminative ability and minimizes redundancy, thereby mitigating the risk of overfitting in high-dimensional feature space. This stratified methodology is empirically substantiated by the findings of the proposed framework: the integration of multi-layer, multi-architecture features with MRMR attained 99.9% accuracy utilizing merely 350 features. The findings indicate that deliberate dimensionality reduction improves generalization despite the framework's initial complexity. Moreover, the design of the proposed framework addresses the particular challenges associated with thermography. In contrast to mammography or MRI, thermal imaging does not possess standardized protocols for anomaly detection, and its diagnostic significance is predominantly based on dynamic thermal patterns rather than static structures. As a result, traditional deep learning frameworks designed for structural imaging could demonstrate suboptimal performance when directly employed in thermal imaging. The proposed framework methodically integrates multi-scale features and meticulously optimizes their representation to compensate for the inherent fluctuation and subtleties of thermal data. The intricacy of the suggested approach is a deliberate attempt to reconcile reliability and computational efficiency. The utilization of compact CNNs, along with efficient feature transformation and selection methods, optimizes the workflow by removing superfluous preprocessing steps such as image enhancement and segmentation. This design decision improves classification performance and guarantees the framework's applicability in resource-limited settings. The enhanced accuracy attained confirms the essentiality of these methodological selections, highlighting their significance in promoting non-invasive, radiation-free breast cancer detection. Regarding the importance of minimizing model complexity and size, the study acknowledges that medical research settings frequently possess substantial computing resources. It is essential to highlight that the suggested CAD framework is intended for both well-equipped medical facilities and resource-limited environments, such as rural clinics, mobile health units, and developing areas with restricted access to advanced computing infrastructure. The proposed CAD framework aims to solve this problem by employing lightweight CNN topologies-MobileNet, EfficientNetB0, and ShuffleNet-so that the framework stays both accurate and computationally lightweight. Besides, smaller models also provide much faster inference time, which is of great importance in real-time diagnostic applications and is conducive to rapid decisions on medical treatment without dependence on heavy-duty hardware. Besides, lightweight models are energy-efficient, in line with the more holistic movement towards sustainability in artificial intelligence work.