## 5 Results
### Explainability Analysis  
This section provides the visual representation and interpretability analysis of the classification outcomes achieved by the three CNN models, leveraging the LIME (Local Interpretable Model-agnostic Explanations) methodology [66]. LIME is applied to produce comprehensible heatmaps that emphasize the most significant areas in thermographic photos of breasts that prompted the models to identify a case as normal or abnormal. Overlapping these clarifications on the original photos provides insight into the decision-making processes of each CNN structure, thus improving transparency and facilitating clinical interpretability. LIME is an explainable AI methodology that offers locally interpretable approximations of the behavior of complex models, including deep learning-based CNNs, facilitating a clearer understanding of the input features that most significantly influence a specific prediction. This method is particularly advantageous in medical image classification as it facilitates the visualization of the regions within a thermogram that the CNNs prioritized during their decision-making process. This interpretability is essential for verifying whether models are identifying clinically significant patterns instead of depending on artefacts or extraneous correlations. LIME generates heatmaps that emphasize areas of significant contribution, enabling clinicians to evaluate whether the model is concentrating on pertinent features, thus verifying its predictions.  
Figure 6 illustrates the LIME approach used for three CNN architectures; EfficientNetB0, MobileNet, and ShuffleNet across normal and abnormal instances derived from thermographic breast images. The initial column presents the original thermogram, whereas the following columns exhibit the overlaid LIME heatmaps for each CNN. These heatmaps employ warmer hues (e.g., red) to signify areas with greater impact on the classification result. The LIME visualizations for the \"abnormal\" cases indicate localised areas of increased temperature or asymmetrical thermal patterns, frequently linked to pathological alterations such as inflammation or tumor formation in breast tissue. EfficientNetB0 seems to identify more accurate and localised thermal anomalies, emphasizing smaller yet highly significant areas within the breast region. Conversely, MobileNet and ShuffleNet emphasize wider thermal distributions, potentially identifying generalized patterns of heat variation along the breast contour. These differences may indicate variations in feature extraction abilities resulting from architectural design and computational efficiency limitations.  
In normal instances, the LIME heatmaps demonstrate a more homogeneous distribution of the thermal contributions, with no identifiable focal hotspots observed in the breast region. This indicates that the models failed to detect any unusual thermal patterns  
Figure 6: Visual representation of the LIME XAI approach for the three CNNs to interpret how decisions were made for each CNN in case of normal and abnormal cases.  
commonly associated with abnormalities. These explanations correspond with anticipated thermal symmetry and typical vascular patterns observed in healthy individuals. The uniformity of these visual interpretations among the three CNN models strengthens confidence in their dependability and clinical validity. Furthermore, the correspondence between model explanations and established thermal properties of normal and abnormal breast tissue reinforces the reliability of the decision-making procedure of the underlying CNNs. The visual representation shown in Figure 6 illustrates the effective application of LIME to improve the interpretability of CNN-based classification systems in breast thermography, providing significant insights into model behavior and fostering increased trust and transparency in AI-assisted diagnostic decisions.