## 2 Related Work  
Machine learning models and deep neural networks are often characterized as \"black box\" models due to their high complexity, resulting in a lack of readily available explanations for their predictions [15]. While these models excel in accuracy, understanding the rationale behind a specific prediction can be a formidable challenge. Many papers focus on the automation aspects of using AI for breast cancer classification through thermal imaging. For example, [PERSON] et al. [19] introduced a novel learning-to-rank (LTR) method combined with handcrafted features. By utilizing HOG with 288 feature vectors, their method achieved an accuracy of 95.80% using a Multi-Layer Perceptron (MLP) classifier. Different DL methods have been applied to the DMR-IR dataset. [PERSON] et al. [20] used textural and spectral features. The relevant features were selected by a Genetic Algorithm (GA). The model achieved an accuracy of 96.15% on the DMR-IR dataset. [PERSON] et al. [21] applied Particle Swarm Optimization (PSO) to optimize the hyperparameters of a CNN model named VGG-16. [PERSON] et al. [22] extracted features using a SqueezeNet 1.1 model and a hybrid of the Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) for feature selection, achieving an accuracy of 100%. [PERSON] et al. [23] applied transfer-based deep learning methods on a static DMR-IR dataset, trained by fusing several views of thermograms. The dataset included 239 images of healthy individuals and 232 images of sick individuals. Their model achieved an accuracy of 93%. Nevertheless, there are relatively few papers focusing on methods used to aid and assist healthcare professionals or lay individuals in understanding the predictions of machine learning models in breast thermogram images. In a recent study [24; 25], authors proposed an approach based on Bayesian networks (BNs) with CNNs. The authors emphasize the utilization of a BN, which is renowned for its probabilistic and graphical data representation to enhance the interpretability of model predictions. These models extract relevant features from thermography images, which Bayesian networks can then interpret to make informed and explainable diagnostic decisions. In a study conducted by [PERSON] et al. [26], the goal was to evaluate the diagnostic capability of thermographic variables for distinguishing patients suspected of having breast cancer from healthy individuals. The paper employs Bayesian networks for analysis, chosen for their ability to reveal interactions between attributes and classes and interactions among attributes themselves. This unique capability allows for a visual identification of which attributes influence the outcome and how they are interconnected. The results indicate that, while other models like Multi-Layer Perceptrons (MLP) and decision trees demonstrate a comparable performance, they lack explanation power. The paper suggests that a deep CNN with transfer learning achieves sensitivity levels similar to those of human experts, even in datasets with a low prevalence of breast cancer. A paper by [PERSON] et al. [27] suggests that a hybrid of deep CNNs and edge detectors can achieve sensitivity levels comparable to those of human experts, even in datasets with a low prevalence of breast cancer. Additionally, they utilized Class Activation Mapping (CAM) into the model. While the paper does not explicitly mention the integration of external interpretability beyond CAM, CAM itself serves as a form of explainability by highlighting the regions of interest in the thermograms that contribute to the network's decision-making process. Considering the advancements of AI for breast cancer detection using thermography, there remains a crucial avenue for improvement, where the scope is placed on optimization-based metaheuristics, a research topic that has garnered significant public attention. Furthermore, the demand for elucidating the distinctive contributions of specific models to predictions is more critical than ever.