###### Abstract
Non-aggressive contrast agents are required to perform thermometry inside breast tissue. Because of its non-ionizing radiation nature, thermography can be applied as many times as may be required and it can be used in women of any age, and in women with dense breast tissue [10]. When only superficial thermography (IR thermography) is available, it is difficult to accurately establish the tumor size and depth; however, combining internal and superficial thermal data, it is possible to overcome this limitation. Like all other techniques, thermography is susceptible to being technologically enhanced to reach desired performances regarding spatial and thermal resolution. In this work, a discussion about the thermal of the current screening methods is presented, taking as a reference the results obtained using the proposed model. Early detection methods for breast cancer, based on temperature estimation, are an area that has been studied for years. The observation and analysis of superficial temperature variations based on infrared thermography is the most common approach [9; 11; 12; 13; 14; 15; 16]. Conversely, there are studies based on technologies such as magnetic resonance imaging (MRI) and microwaves (MW) that can obtain temperature maps of internal structures. As a support tool, computational models that allow the simulation of thermal phenomena associated with pathologies in the breast have been proposed in order to understand and predict the thermal behavior of related tissues. Due to the relevance of IR thermography in breast cancer detection research, a great variety of modelling works were performed to relate surface temperature distribution with thermal and physical characteristics of tumor. Such is the case of the study conducted by [PERSON] et al. [17], which modelled the cases of tumors smaller than 20 mm at different depths and polar angles and at varied thermal parameters. As a result, they defined two characteristics as indicators for estimating the size and location of a tumor: maximum temperature difference and half temperature difference length. [PERSON] et al. [18] implemented a model to assess breast surface temperature variations produced by changes in thermal and metabolic parameters, as well as in tumor location and size. The model considered three breast sizes. The results showed that (considering a tumor size = 1 cm), for the largest breast, the surface temperature variation for the innermost tumors was below 0.2 \({}^{\circ}\)C, while for the smallest breast, temperature variation was below 0.5 \({}^{\circ}\)C. Tumors of 0.5 cm produced small temperature increments on the surface, even for small breast sizes and non-deep locations (2 cm from surface). In the study presented by [PERSON] et al. [19], tumors with sizes in the range from 10 mm to 30 mm, located at different positions, were considered. The authors concluded that temperature distribution on the surface changed with the size and depth of tumors; however, a single steady-state thermogram was not enough to estimate these parameters. A one-dimensional model of breast tissue, composed of the epidermis; dermis; subcutaneous tissue; glandular, tumoral, and muscular layers; and finally, the thoracic wall, was developed by [PERSON] et al. [10]. The aim was to obtain the effect of tumor size and depth on the temperature of all the tissue layers and the breast surface temperature. Steady- and transient-state studies were conducted. [PERSON] et al. [20] extended the thermal analysis scope, proposing forward and inverse implementation; the first models the internal and superficial temperature distributions associated with hypothetic breast cancer cases. These data were fed into a dynamic neural network to train to obtain the internal temperature distribution from its corresponding surface thermal distribution. In the inverse stage, a neural network was used to estimate the internal temperature distribution from real thermal images. Finally, tumor parameters were extracted from the internal temperature profile.