## 1 Introduction  
Cancer is the foremost cause of death worldwide and in the United States. Based on the American Cancer Society and World Health Organization (WHO) reports, newly diagnosed cancer cases in 2021 accounted for an overall estimation of 608,570 cancer deaths, where breast cancer alone was 30% of the overall cases [1, 2]. Despite better survival rates and developments in different imaging modalities for screening and therapy, breast cancer is the most fatal cancer among women (second-most common cancer) [1]. Early detection of breast cancer has a crucial role in disease treatment planning and patients' survival. This study proposes a deep learning-based model for dynamic thermography imaging for a clinical breast exam (CBE) before performing mammography. We hypothesize that deep learning features can track thermal heterogeneity in breast tissue, which may associate with the angiogenesis and vasodilation caused by cancer metabolism. Since the 1960s, mammography has been a gold standard method for breast cancer screening. Studies reported some variabilities in the screening results with this modality, facing various clinical factors such as age, breast density, type of malignancy, and family history [3; 4; 5; 6; 7]. Fibrocytic breasts, hormone replacement therapy, and dense breasts undermine the strength of mammography imaging for accurate diagnosis (and screening) [8; 9; 10; 11]. As an alternative, magnetic resonance imaging (MRI) is often used as it is relatively expensive and has lower specificity than mammography [12; 13]. A CBE is performed by clinicians, which helps for detecting breast cancer with reasonable accuracy, but it is not usually employed alone [14; 15]. The proposed system can be used in combination with CBE to increase the detection rate of breast cancer as a noninvasive low-cost tool. Infrared imaging captures thermal radiation emitted from tissue within an 8-10 \(\upmu\)m bandwidth. Skin's emissivity is close to black-body emissivity (0.98) [16; 17], which can transfer thermal fluctuations in such a way as to be observed with an infrared camera. The main source of thermal radiation is blood circulation, which becomes more heterogeneous due to the vasodilation and angiogenesis of tumor-adjacent tissues in abnormal tissue [18; 19]. Additionally, metabolic activity in malignant tissue correlating with nitric oxide and estrogen expression causes increased temperatures, which can be detected through the skin [6; 19; 20; 21; 22]. In malignant neoplasia, the tumors' growth is dependent on angiogenic sprouts, such that without this vascular support, tumors cannot receive vital nutrients [23; 24]. Angiogenesis also promotes breast cancer progression and metastasis [25] through the cessation of C-C chemokine ligand 2 (CCL2) inhibitors [26]. The vascular endothelial growth factor (VEGF) is an important angiogenic factor for growing vascularity and providing nutrients to cancer cells [27]. Several studies have tried to detect such metabolic activities through different imaging tools, such as Raman spectroscopy [28; 29] and infrared imaging [30; 31; 32]. In this study, we focus on the application of infrared imaging as an indicator of cancer's presence (see Figure 1). The application of infrared thermography in breast cancer screening involves computational methods to extract thermal heterogeneity. There are many methods for thermography that capture variance and reduce the dimensionality of thermal images [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. In this study, we propose an infrared imaging system equipped with deep learning-based ther  
Figure 1: The schematic of angiogenesis (blood vessel formation) surrounding tumor cells in the breast area and vasodilation, which can be captured through short acquisition interval thermal imaging using dynamic thermography. momics to assist the clinician in the early diagnosis of breast cancer combined with CBE. Figure 2 shows the workflow of the proposed method.