## 1 Introduction  
Breast cancer stands as one of the leading causes of mortality in women worldwide, highlighting the need to implement accessible and effective diagnostic tools facilitating early risk detection [1]. In Mexico, this disease is a critical public health issue, with an increasing incidence rate and an approximate rate of 27 cases per 100,000 people [2]. Many cases remain undetected until they reach advanced stages, limiting the therapeutic options and reducing the survival rates; therefore, early detection is essential [3]. However, conventional diagnostic methods have limitations, especially in patients with a high breast density. The breast density is a key factor, as an increase in it is associated with a higher risk of breast cancer diagnosis. Various factors, especially hormonal ones, influence changes in the breast density over time. Studies have shown that women with a higher breast density have elevated estrogen levels compared to their lower-density counterparts, which is an additional risk factor for breast cancer [4]. Furthermore, a higher breast density can reduce the effectiveness of conventional mammograms, making early lesion detection more difficult and increasing the likelihood of false negatives. Conventional mammograms remain the reference technique for breast cancer diagnosis due to their high sensitivity in detecting microcalcifications and small lesions. However, they have certain limitations, such as exposing patients to ionizing radiation, difficulties in detecting early-stage lesions in patients with a high breast density, and the possibility of false negatives in some cases. Complementary technologies such as ultrasound, magnetic resonance imaging (MRI), and breast tomosynthesis have been developed. Ultrasound is particularly useful for differentiating cysts from solid masses without the need for ionizing radiation; however, it has limitations in detecting microcalcifications [5]. MRI, on the other hand, offers high sensitivity and is particularly beneficial for patients with a high breast density or an increased risk of developing breast cancer. Breast tomosynthesis provides three-dimensional images, reducing tissue overlaps and improving the visualization of small lesions. However, its application requires a higher radiation dose compared to that of conventional mammograms [6; 7]. Various studies have shown that combining multiple diagnostic techniques, such as mammograms, ultrasounds, and emerging methods like Electrical Impedance Mammography (EIM), can significantly improve the accuracy of identifying suspicious lesions [4; 8]. Unlike conventional mammograms, EIM is non-invasive and radiation-free and can be performed on young women, making it a safe alternative to more traditional methods [9]. Additionally, it enables real-time evaluation and complements the information obtained using conventional techniques for the early detection of breast cancer [10; 11]. EIM is based on the analysis of the breast density and electrical conductivity, providing a set of images representing different layers of the breast tissue [12]. This layered representation enables a more detailed view of the internal characteristics of the tissue, facilitating the identification of potential anomalies. However, handling these images individually could generate information redundancy and complexity in the extraction of relevant features to detect possible lesions. Currently, EIM images are analyzed independently [13; 14; 15], which can hinder the identification of subtle tissue changes as well as the assessment of potential anomalies. Figuring out how to integrate the layers into a single representation while preserving the relevant features at each depth layer could help to address the current constraints. In this paper, the detection of anomalies related to breast cancer in EIM mammograms was addressed by casting it as a binary classification task. Given the nature of these medical images, this task can be accomplished by analyzing the images from different perspectives.