## 6 Discussion
### Shortcomings and Possible Future Directions  
The suggested CAD paradigm exhibited notable enhancements in breast cancer identification through thermal scanning; however, multiple drawbacks require consideration. First, it only considered identifying breast cancer without classifying the subcategory of breast cancer or whether it is benign or malignant. Second, the study employed several lightweight CNN structures--MobileNet, EfficientNetB0, and ShuffleNet--which, although efficient, may fail to capture specific complex characteristics that deeper or custom-designed networks, or other newer architectures such as vision transformers (ViT) could offer. Exploring the incorporation of more sophisticated topologies or ViT may enhance performance considerably. A further constraint pertains to the generalizability of the findings. The CAD paradigm was evaluated using a certain dataset that may not encompass all possible differences in breast cancer instances, especially those affected by demographics or environmental variables. Extending the assessment to larger, more heterogeneous datasets and conducting cross-validation on external datasets is crucial for ensuring robustness and wider applicability. The system presupposes the availability of high-quality, preprocessed thermal images; however, managing discrepancies in image quality and artefacts in practical situations poses an obstacle. Creating preprocessing algorithms capable of adapting to suboptimal data contexts would enhance the practicality of the CAD system for clinical application. Furthermore, this study did not use XAI to interpret the manner in which decisions were made using deep learning models. The future plans may encompass the real-time deployment of the CAD system for expedited diagnostic support and its incorporation into telemedicine platforms, especially in areas with limited access. Investigating XAI methodologies that clarify the model's decisions would improve clinical acceptability by offering transparency and practical recommendations for healthcare practitioners. Enhancing the system via multimodal integration of other scanning modalities such as mammogram and ultrasound, using more datasets to increase generalizability, classifying breast cancer into subcategories, and using more recent deep learning models such as ViT which could enhance breast cancer diagnostics. Regrettably, the publicly accessible DMR-IR dataset lacks comprehensive clinical annotations, including tumor size, stage, and histopathological validation, which are either absent or reported inconsistently. This limitation signifies a more extensive challenge in thermography-based research rather than a particular deficiency of the study. The study acknowledges the importance of integrating mammography data and BIRADS scoring to correlate thermographic findings with recognized radiologic reporting systems. Such an approach would undoubtedly enhance the analysis and facilitate the contextualizationization of each case within a standardized framework. This study concentrated solely on thermographic images due to dataset limitations and aimed to create a standalone, radiation-free alternative for situations where mammography is either unavailable or contraindicated. Although multimodal integration represents a promising avenue for future research, it was outside the parameters of the present study. The limitations identified pertain not only to the specific dataset but also to the overall accessibility and standardization of thermographic data in medical imaging research. These omissions may create biases and influence the generalizability of the results. The author advocates for the creation and dissemination of more extensive, clinically annotated thermographic datasets that encompass staging information, lesion type classifications, and correlations with other imaging modalities. These efforts will be crucial in progressing the field and ensuring that thermography-based CAD systems can be thoroughly validated for practical clinical application.