## 1 Introduction  
Breast cancer is a profoundly distressing disease. Screening programs for early breast disease detection significantly contribute to reductions in the mortality rate among women. These programs save lives by detecting conditions at their initial stages when treatment is more effective and less costly. Medical imaging modalities are employed for breast cancer diagnosis, as well as the differentiation of malignant from benign breast tumors, including mammography, magnetic resonance imaging (MRI), ultrasound, computed tomography (CT), positron emission tomography (PET), and thermography. Mammography, while widely used, presents specific limitations. These include challenges in visualizing smaller tumors [1], inadvisability for use in younger women and those with dense breast tissues [2], considerable cost and time requirements [3], and physical discomfort due to breast compression. Moreover, concerns about the potential carcinogenic effects of cumulative ionizing radiation exposure have been raised [4]. The high incidence of false-positive results further contributes to patient anxiety and unnecessary procedures [5]. In this context, Infrared Thermography has emerged as a promising and robust screening tool for early cancer detection [6; 7]. Thermography offers distinct advantages, such as being painless, non-invasive, non-contact, and cost-effective [8]. Notably, it is particularly well suited for screening younger women, patients with dense breast tissue, and pregnant or nursing women, as it does not involve ionizing radiation exposure [9]. In health systems, breast cancer diagnostics have benefited from computer-aided diagnosis (CAD) systems, streamlining the analysis process and minimizing errors. These systems typically consist of several steps, from image preprocessing to classification, where feature extraction and selection play vital roles. Extracting pertinent features is essential for capturing subtle patterns indicative of early-stage breast cancer, whether through meticulously crafted methods or advanced deep learning approaches [10]. However, the current narrative predominantly emphasizes the accuracy and automation aspects of these systems, often achieved by training models on extensive datasets to recognize patterns in medical data. Yet, amid these advancements, there exists a notable gap in the discourse--the lack of attention to model interpretability [11]. In this paper, we proposed an interpretable computer-aided diagnosis (CAD) while delving into the relationship between pattern recognition and machine learning. Understanding this connection is paramount in addressing the complexity of medical data. Pattern recognition, within its broader domain, is fundamentally concerned with identifying regularities or inherent patterns within datasets. This field embraces a wide array of techniques and methods designed to identify patterns across various data types, spanning images, signals, and sequences. The attainment of pattern recognition can be realized through two primary approaches: manual and automated. Manual pattern recognition often relies on human expertise and heuristic approaches [12], depending on the nuanced judgment of experts to identify patterns, while automated pattern recognition employs computational methods to autonomously detect and outline patterns embedded within the data [13]. Machine learning, a subset of artificial intelligence, is exclusively dedicated to developing algorithms and statistical models that empower computers to learn from data and subsequently make predictions or decisions, encompassing a vast spectrum of techniques, including supervised learning, unsupervised learning, reinforcement learning, and hybrid methodologies. On the other hand, healthcare professionals need to understand how AI systems arrive at their recommendations or decisions, especially when these decisions can have significant real-world impacts.