###### Abstract
It is difficult to diagnose cancer in early stages due to the size of the tumor, which generally does not induce pain. Existing methods are not conducive for regular mass screening in short intervals, and they are not suited for regular breast self-examination (BSE) as promoted by WHO [6]. Some are harmful, painful, or uncomfortable, while others can be expensive and not readily available. The effectiveness of the approaches can also be dependent upon factors such as gland density. As a result, many breast cancer cases are discovered in the late stage when the tumor has become big enough to induce pain and other symptoms including lymph node damage, drawn nipples, and discolored or destructed breast skin [3]. Treatment in these stages can be challenging. A non-invasive and inexpensive approach for early breast cancer detection is the examination of the breast skin surface temperature. Body temperature is a popular indicator that is used to check human health status. The effective usage of thermal data in clinical conditions can diagnose many illnesses [7; 8; 9]. It is well known that temperature distribution on the skin depends on factors such as blood perfusion, metabolic rate, and ambient temperature. Any abnormality such as tumor or inflammation can cause the atypical distribution of temperature on the skin surface. Tumors have higher heat generation rates compared with surrounding healthy tissues, thus allowing for the observation of non-homogeneity in breast thermograms with tumors [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. The study by [PERSON] et al. [13] compared the accuracy of infrared thermal imaging (IRTI) and mammography. The results showed that mammography had higher accuracy (95.38%) than infrared thermal imaging (92.31%). At the same time, thermography was able to detect three cases of malignant tumor, whereas mammography could not. [PERSON] et al. [14] divided the breast into seven layers including the tumor tissue and studied the temperature distribution on the breast surface. It was concluded that in the case of applying dynamic thermography in order to improve tumor detection, the healthy tissue would reach the steady-state condition earlier compared with the tumor tissue. [PERSON] et al. [15] created various models of breasts with different compositions of glandular and fibrous tissues. It was concluded that the composition of the mammary gland affects temperature distribution on the breast surface. Further work by [PERSON] et al. [16] examined 123 females using Dual-Energy X-ray Absorptiometry and an Infrared camera. The results of the study revealed the influence of the fat percentage on the temperature distribution on the surface of the breast that is captured by IR camera. Researchers [2; 8; 17; 18; 19; 20; 21; 22] suggested that improvements in the quality of thermal cameras and the development of machine learning techniques can significantly enhance thermography as an adjunct tool for breast cancer detection. They also stated that thermography could achieve greater advancement if used in combination with segmentation, extraction, or numerical simulation. According to [PERSON] et al. [17] and [PERSON] et al. [18], numerical simulation allows for the study of the relationship between thermal behavior (heat generation, thermal conductivity, blood perfusion, etc.) and breast physiological states. The most important part of such study is geometrical modeling, as the model is used for the calculation and definition of the variables influencing the physiological change of the breast state. In this regard, it is important to use the real geometry of the breast, consisting of different layers [14; 19; 20; 21]. Therefore, in this study, we propose that the combination of thermography and 3D scanning of the breasts can be used for the early detection of tumors and for further development of BSE through the miniaturization of the equipment and Artificial Intelligence (AI).