## 4 Conclusions and Discussion
Convolutional neural networks for the \"SIMULATION\" sample give 71.5 percent accuracy in determining the location of the tumor based on the criterion of being in a given breast sector. We note the good agreement of this result with the estimates when using a multilayer perceptron network within 62-64 percent [108]. 4. An important feature of the MWR diagnostics is the ability to simultaneously have high values of sensitivity and specificity. As a rule, mammography, ultrasound and MRI methods are better at detecting cancer patients (high sensitivity), but poorer at recognizing healthy people (low specificity). This is due to the difference in physical methods, when mammography, ultrasound and MRI are based on the determination of structural changes in tissues. The MWR method detects temperature anomalies caused by inflammatory processes due to disease. 5. We propose a new 17-point breast examination scheme (instead of the traditional 9-point scheme), which allows us to build a better picture of temperature fields. Our analysis showed an increase in both sensitivity and specificity for this modified diagnostic algorithm. Using only the \"REAL\" sample for the CNN does not give good results because the sample size is small and the number of Sick patients is also small, which is critical for CNN. The combined dataset provides satisfactory and close results for SVM and CNN (see Table 5). Note that the SVM algorithm turns out to be worse when working with feature spaces for breast oncology [12]. The 3D modeling makes it possible to create a model set with different internal structures of the breast. Each such model differs in the geometric parameters of such main components of the breast as the breast lobes, laceiferous sinus and ducts, adipose tissue, arterial and venous subsystems, and others. The size and shape of the breast also varies. The natural variability of the geometric characteristics of \(\vec{\mathcal{G}}\) is quite large, and the question of the influence of this uncertainty requires additional studies based on the construction of an even larger \"REAL\" sample, which apparently is necessary for processing breast cancer screening data when using MWR in practice. However, the wavelength for MWR is \(\geq\) 2 cm, so the uncertainties in the sizes and localizations of biocomponents on smaller scales make a small contribution to the spatial temperature distributions. Thus, the statistical properties of \"REAL\" and \"SIMULATION\" datasets are consistent, providing a measurement error of approximately 0.2 \({}^{\circ}\)C [32]. The advantages and limitations of the proposed method for processing MWR thermometric data are due to both the method of microwave temperature measurement itself and the quality of dataset construction based on computer modeling. The \"SIMULATION\" sample quality is ensured by using realistic models of the internal structure of the breast, which contains all the main 3D components of this complex biotissue. Transition from the commonly used multilayer breast model [65; 72; 73; 74; 109] to our multicomponent 3D internal structure seems to be a necessary step to build a better \"SIMULATION\" sample. Multilayer models give formally higher values of sensitivity and specificity [110], because the tumor as a hot zone stands out very well against the background of an almost uniform temperature distribution. Therefore, reliable detection of such a tumor is an artificial result due to the roughness of the approach, and such multilayer models poorly describe the real properties of the breasts. We emphasize that the proposed method for applying combined datasets based on both real medical measurements and numerical simulations of MWR measurements can be effective for machine learning in solving a wide range of problems in diagnosing various organs and diseases.