## 2 Related Work
Moreover, the inverse heat transfer problem was solved using sequential quadratic programming to determine the thermal conductivity and blood perfusion of breast tissues and cancers. The data confirmed through the implementation of the research technique demonstrated that thermophysical properties can be estimated using infrared images. Finally, the empirical correlation between breast thermal conductivity and patient age provided a satisfactory response for the cases analyzed. Studies conducted by [16] refer to the use of numerical simulations of breast cancer using the Pennes equation and COMSOL software to analyze the temperature change of breast surface and factors that affect temperature variation. In addition, two locations of cancer were used in the breast, with depths of 4.5 cm and 5.5 cm. Moreover, normal- and high-density breast tissue were specified with tumors with different sizes of 1 cm, 1.5 cm, 2 cm, and 2.5 cm. In addition to simulating breast layers, perfusion rate, density, and the specific heat capacity of the blood were also simulated. The results indicated that the temperature range changed on the surface of the breast from 0.1 \({}^{\circ}\)C to 1.41 \({}^{\circ}\)C.  
In [17], researchers referred to the development of a standard protocol for taking thermal images and the challenges facing numerical calculation in the changing temperature in healthy breasts. In addition, a 43 m\({}^{2}\) room was used to take thermal images from the patients, with a 1-m distance from the thermal camera, while adjusting room humidity and temperature. Moreover, breast tissue density was added in order to study physical parameters using ultrasound. Additionally, the software FLUENTTM was used to simulate numerical factors affecting temperature changes of the breast surface. The software analyses thermographic images enable the temperature at any point of an acquired digital image to be known. The results showed that it is possible to determine these physical properties using the maximum temperature from the program. The work in [18] used numerical simulation of factors that affect heat transfer, such as specific mass, specific heat, blood perfusion rate, and thermal conductivity of the tissue. In addition, the COMSOL program and Pennes equation for thermal representation were used, and factors affecting heat transfer in the breast were studied. However, only three positions were used to simulate breast cancer to a depth of up to 3.8 cm. In addition, the values of these factors were changed to study the change in heat transfer to the surface of the breast. Moreover, a thermal camera was used to take thermal images of fake tissue equipped with electrical resistors to simulate the effect of breast cancer heat. The results indicated that the thermal conduction of healthy tissue surrounding the tumor does not affect tumor location, with an error rate up to \(\pm\)1 mm. The researcher in [19] simulated breast cancer in terms of tumor size and location in the breast, using the COMSOL software. In addition, the Pennes bioheat transfer equation and artificial networks were used to achieve high detection accuracy. In addition, heat was generated to simulate the tumor in two models. The first generates a constant temperature and the second generates variable heat, thereby generating 447 temperature data vectors in each case. Moreover, different values of noise were added to thermal images. The results indicated that the detection accuracy of breast cancer is 98% using artificial intelligence networks. In addition, given that the breast thermogram is within 0.1 noise levels, a neural network approach can be adopted to retrieve the tumor location and radius with reasonable accuracy. The study's main goal in [20] was to identify and calculate the geometric centers of breast tumors using skin surface temperatures. To determine the distribution of skin temperature among the three main forms of breast cancer, the 2D anatomical geometry of the breast was simulated using COMSOL software.