## 2 Previous Works  
Current research indicates that the majority of prior studies have employed thermographic images from various disciplines, with minimal application in the medical field including breast cancer detection. Furthermore, numerous studies have combined AI methodologies with alternative diagnostic modalities for the analysis of breast cancer. Nonetheless, a limited number of studies have investigated the synergistic application of thermography and AI methods for breast cancer detection, underscoring a deficiency in the existing literature. The research [36] presented a U-Net-based approach for the automatic segmentation of breast zones, removing interference from adjacent anatomy. A tailored CNN was subsequently created to categorize breast thermograms as normal or abnormal, attaining a remarkable accuracy surpassing 99 percent on the DMR-IR dataset. An improved CNN was created in [27] to produce heatmaps from thermal breast images. These heatmaps, in conjunction with Fuzzy C-means clustering and thermal profiling, exhibited a classification success rate of 96.8 percent and a specificity of 93.7 percent. Explainable AI (XAI) methodologies have improved comprehension of models and efficacy. Customized attributes integrated with SHAP-based explanations yielded substantial performance enhancements, achieving an accuracy of 98.27 percent and an F1 score of 98.15 percent [37]. In the research [38], Bayesian networks were merged with CNNs to construct two approaches for the early identification of breast cancer. The combined effect of XAI with thermal imaging and clinical information attained an accuracy of 84.07 percent whereas an independent CNN model attained an accuracy of 90.93 percent. Preprocessing and extracting features are crucial in improving thermogram-based CAD systems. A study [39] used morphological procedures and curvelet-texture analysis to extract statistical and textural features, attaining an accuracy rate for classification of 93.3 percent using a cubic support vector machine (SVM). A separate study [40] combined multi-view thermal imaging with clinical data, employing asymmetric dispersion and level-set techniques for segmentation. The retrieved texture attributes, in conjunction with kernel principal component analysis for dimensionality reduction, attained an accuracy of 96 percent. The study [28] employed a segmentation method that combines the curvature parameter k with the gradient vector flow, and for identification, it introduced a CNN applied to the fragmented breast, achieving an accuracy of 100 percent. Transfer learning methodologies have demonstrated potential in the detection of breast cancer. A research investigation [29] employed the pre-trained VGG16 method, enhanced through data augmentation and normalization, attained remarkable outcomes, involving 99.4 percent accuracy. This methodology was contrasted with techniques such as SVM and Gradient Boosting, highlighting the effectiveness of pre-trained deep learning algorithms for thermogram analysis. A different approach, called LC-SCS, balanced high accuracy and computational efficiency by classifying thermograms using pre-trained networks such as VGG-19 and ResNet-50. The LC-SCS model attained an accuracy of 94 percent [41]. Furthermore, another system leveraged region of interest (ROI) retrieval and a multi-inputdesign, merging thermal photographs with clinical data. Models developed through this methodology exceeded single-input models, attaining an accuracy of 90.48 percent [42]. A different approach deployed a customized CNN, incorporating preprocessing through morphological operations and object-oriented segmentation, yielding remarkable outcomes and illustrating the effectiveness of such methods in clinical applications [30]. An alternative method examined the processing of thermographic images through the integration of deep learning frameworks and evolutionary algorithms. The methodology involved decomposing images into red, green, and blue channels and then analysing each channel with distinct CNNs.