## 1 Introduction  
Breast cancer remains a major global health issue, primarily impacting women, with around 2.3 million cases recorded in 2020 and considerable mortality rates [1, 2]. Healthcare practitioners utilize various diagnostic methods to identify and treat this disease, encompassing clinical assessments, self-screening, and sophisticated imaging technologies. Mammography is a key diagnostic technique that proficiently detects microscopic tissue alterations and structural irregularities, although it has limitations in visualizing dense breast tissue [3; 4]. Alternative diagnostic modalities, including ultrasound and magnetic resonance imaging (MRI), provide further insights, each presenting distinct benefits and challenges in differentiating between benign and malignant conditions [5; 6]. Tissue biopsy remains the most definitive testing method, offering cellular-level analysis despite its invasive characteristics and the necessity for specialized equipment and expertise [7]. Current research progressively emphasizes the creation of more holistic, patient-centered diagnostic protocols that incorporate other screening methods avoiding the limitations of present screening modalities. In present medical examinations, infrared (IR) thermal imaging has emerged as a powerful non-invasive scanning technique that uses physiological thermal radiation from biological surfaces to illuminate fundamental physiological functions [8]. Thermography employs IR cameras to capture the thermal patterns of the breast. The variation in temperature on the breast surface can thus indicate the presence of a tumor [9]. The fundamental principle of IR image diagnosis is that uncontrolled cellular proliferation results in an elevated metabolic rate, necessitating increased blood flow compared to adjacent tissue. The excess heat produced is transmitted to the tissue adjacent to the tumor, resulting in a temperature increase on the breast surface. The temperature increase is detected through IR imaging to identify the tumor [10]. This diagnostic method shows significant promise in breast cancer screening, especially for individuals with dense breast tissue, providing a radiation-free, economical alternative to traditional imaging techniques such as MRI and mammography [11]. The non-invasive characteristics of the technique, along with its ability to identify thermal abnormalities prior to structural tissue alterations, establish IR thermography as a promising avenue for early diagnostic interventions, attracting increasing academic interest in its extensive clinical potential [12]. The present practice of medical diagnosis has been transformed by emerging artificial intelligence (AI) frameworks, particularly neural network structures, and advanced machine learning approaches, which have significantly surpassed conventional diagnostic methods [13]. The use of computing intelligence methods is exhibiting exceptional diagnostic accuracy in various medical fields, with computer-assisted diagnosis (CAD) systems utilizing deep learning techniques attaining notable diagnostic efficacy [14] in oncological evaluations [15; 16], lung abnormalities [17; 18], encompassing neoplastic disorders in dermatological [19; 20], reproductive, hematological, gastrointestinal [21], and breast cancer detection systems [22; 23; 24]. Convolutional neural networks (CNNs), a leading deep learning architecture, have demonstrated remarkable diagnostic proficiency in medical image analysis, especially in intricate imaging techniques like thermographic breast cancer screening [25]. Such cutting-edge computational approaches provide physicians with proficient diagnostic assistance, facilitating quicker, independent, and more complicated disease detection procedures; however, they also pose computational complexity issues concerning parameter optimization and possible overfitting [26]. Existing breast cancer CAD techniques exhibit considerable technological obstacles notwithstanding significant progress in diagnostic imaging [1].