boundary do not depend continuously on the conductivity inside the human body. Thus, a big change in conductivity does not necessarily correspond to a big change in the voltage measurements. Inversion of this leads to problems during reconstruction. Also, numerical implementations of various algorithms pose problems in terms of stability and accuracy. This wide range of problems makes reconstruction challenging.  
In general, there are two modes of reconstruction, time-dependent and time-independent. Time-dependent reconstruction provides a difference image from one time step to another. This process makes the reconstruction quality better, as systematical errors cancel out. Time-independent reconstruction, in contrast, processes one voltage measurement to a conductivity distribution. This is more complex as factors like uncertainty in the exact knowledge of the geometry and other errors are not suppressed. Thus, time-independent reconstructions are typically of lower quality. The choice of reconstruction mode can have a significant impact on the accuracy and effectiveness of EIT in various applications. Some applications like breast cancer force the choice of time-independent reconstruction, as for one measurement the system behaves quasi-static. During one examination, the breast cancer will not change in such a way that a time-dependent reconstruction would be helpful.  
Breast cancer has the highest mortality associated with cancer among women worldwide. In 2022, 2.3 million women around the world suffered from breast cancer, while 0.68 million died from it [9]. However, early detection of breast cancer can rapidly decrease these rates. If the cancer has not spread, the relative 5-year cancer survival rate is 99% [10]. When the cancer has spread regionally in the breast, the rate drops to 86% [10]. Only when the cancer has metastasized to far body regions, the rate drops to 27% [10]. Thus, early detection of breast cancer is vital. The gold standard for non-invasive examination of the breast region for breast cancer is X-ray mammography. However, due to pain from the compression of the breast, many eligible women avoid this opportunity for examination. In Germany, only around 50% of all eligible women participate in the screening [11]. Thus, other techniques for the detection of breast cancer would be helpful.  
Electrical Impedance Tomography (EIT) has emerged as a promising alternative. This technique visualizes the conductivity distribution within the human body, exploiting the fact that cancerous tissue exhibits higher electrical conductivity than surrounding fatty tissue [12]. This contrast aids in image reconstruction and potential tumor detection.  
Despite its potential, the interpretation of EIT images remains challenging due to their low spatial resolution. This issue is further compounded by the fact that EIT is a highly non-linear inverse problem, which often results in reconstruction artifacts that can be misinterpreted as tumors.  
Numerous studies have addressed these challenges. For instance, [PERSON] et al. developed a 3D EIT-based breast cancer detection system [13]. [PERSON] et al. tested the \"MEIK electroimpedance v.5.6\" screening system [14], while [PERSON] et al. designed an EIT screening system integrated into a bra [6]. For a more comprehensive overview of available systems and methodologies, we recommend survey papers by [PERSON] et al. [15] and [PERSON] et al. [16].  
In this simulation study, we assess the performance of three machine learning models--Random Forest (RF), Support Vector Machine (SVM), and an Artificial Neural Network (ANN)--in interpreting EIT images. Our focus is on the auxiliary classification that these models provide, which could be instrumental in aiding any potential reconstruction process. This approach is designed to enhance the interpretability of EIT images and increase the accuracy of breast cancer detection in a simulated setting. Through this work, we aim to contribute to the ongoing efforts to advance EIT as a reliable tool for breast cancer detection.