## 6 Discussion
### Complexity Analysis of the Proposed CAD
tion time of 6525 s. Conversely, EfficientNetB0, comprising roughly 5.3 million parameters and 82 layers, demonstrates markedly greater computational requirements, evidenced by its extended classification duration of 15,870 s. MobileNet possesses approximately 3.5 million parameters and requires 4194 s for classification. These figures correspond with the anticipated computational behaviors of each network, wherein deeper and more intricate architectures inherently entail increased processing overhead. Nonetheless, notwithstanding their disparities in computational expense, all three models function as proficient feature extractors across various network depths; layer 1 (early-level), layer 2 (mid-level), and layer 3 (high-level) prior to being input into the MGSVM classifier for conclusive decision-making. The raw feature dimensions extracted from layer 1 of ShuffleNet, MobileNet, and EfficientNetB0 are 26,656, 62,720, and 62,720, respectively which are massive. Subsequent to the application of DWT, their dimensions approached 417, 980, and 980, respectively, resulting in a classification time ranging from 1.4029 to 2.3946 s. Subsequently, Layer 1 features are subjected to additional dimensionality reduction through NNMF, yielding compacted representations of 70, 40, and 60 features for ShuffleNet, MobileNet, and EfficientNetB0, respectively. This significant reduction demonstrates the efficacy of DWT and NNMF in maintaining discriminative information while reducing redundancy, thus enhancing both computational efficiency and classification duration (0.8532 to 1.1128 s). At layer 2, the initial feature dimensions are larger (544 for ShuffleNet; 1280 for MobileNet and EfficientNetB0) with classification durations ranging from 1.8921 to 3.3868 s; however, following the application of NNMF, they are diminished to 80, 100, and 50 features, respectively. These reductions enable expedited classification times (between 0.8532 and 1.1128 s) without noticeable degradation in diagnostic performance, indicating that the compressed feature sets maintain adequate discriminatory capacity for precise classification. Fusion techniques at both intra-CNN and inter-CNN levels increase complexity while simultaneously improving model robustness by amalgamating complementary information from various layers and architectures. In Scenario II, where features from the same layer of all three CNNs are merged, the input feature dimensions expand to 6 (layer 3), 230 (layer 2), and 170 (layer 1). Notwithstanding this augmentation, the classification durations remain minimal (0.96-0.95 s), signifying that the MGSVM adeptly accommodates the expanded feature space. In Scenario III, employing a multi-CNN and multi-layer fusion method, succeeded by mRMR feature selection, the resultant feature set consists of 350 features. This configuration results in a classification time of 1.4045 s, but it likely provides the most thorough representation by integrating various hierarchical features from multiple CNNs, thereby improving the model's capacity to detect subtle thermal variations indicative of abnormalities. The complexity analysis highlights a distinct correlation among model depth, feature dimensionality, and classification efficacy. End-to-end deep learning methodologies provide substantial representational capacity, but they entail a heightened computational burden. Conversely, feature engineering methodologies such as DWT, NNMF, and mRMR-based fusion facilitate a balance between accuracy and efficacy, rendering them especially appropriate for real-time or resource-limited diagnostic applications. The findings substantiate the justification for the proposed hybrid CAD framework, which exploits the advantages of various CNNs and sophisticated feature processing techniques to attain both elevated diagnostic accuracy and comprehensible decision pathways via LIME-based visual elucidations.