## 4 Results  
This section presents the results obtained by applying the aforementioned methodology for combining the information in the EIM layers, casting the problem as a classification task for experimental purposes using different classifiers, including Logistic Regression and Random Forest, as well as two well-known ensemble methods: bagging and XGBoost. A cross-validation was implemented to ensure data variability using seven folds. All the experiments were implemented using Python 3.10 (Python Software Foundation), including the NumPy and Pandas libraries for data handling, OpenCV and scikit-image for image preprocessing, TensorFlow/Keras for feature extraction using pre-trained CNNs (e.g., VGG19), and scikit-learn for training and evaluating traditional classifiers. Visualization tasks were carried out using Matplotlib version 3.7.5 (maintained by NumFOCUS). Table 3 shows some examples of EIM images labeled with their corresponding BI-RADS grade after applying the integration and fusion methods. The color scale (Figure 1) shows the more dense zones in yellow-red tones, while the less dense zones appear in blue-cyan tones. The integration methods maintained a dense central zone (nipple) with a low level of suspicion (BI-RADS grades 1-2), which is often used as a reference. However, in the case of a high level of suspicion (BI-RADS grades 3-5), the CV appears to fail in maintaining this type of information. When using CV\({}_{\text{CLAHE}}\), the transitions towards the peripheral areas were progressive, improving the differentiation between regions with heterogeneousdensities and facilitating the identification of patterns associated with glandular structures or suspicious lesions. On the other hand, the \(\mathrm{C_{RMS}}\) accentuated the dense regions with more abrupt transitions and tended to thicken the denser areas, which could generate a loss of continuity in the transition towards less dense regions and make interpretation difficult for a clinic, as visual artifacts may be mistaken for real anomalies. Fusion methods also highlighted the dense central zone, although in a more limited manner compared to the integration methods, retaining this information only up to a BI-RADS grade of 3. CLAHE\({}_{\mathrm{F}}\) equilibrated the enhancement of high-density zones, generating a homogeneous representation that minimized visual noise. However, it also maintained the definition of structures and the subtle changes in peripheral areas, which was very useful for highlighting the differences between regions. The results obtained using the Gaussian pyramid and weighted average fusion methods were very similar to those obtained using CLAHE\({}_{\mathrm{F}}\). However, lower definition was observed between adjacent areas in the former. Finally, with Wavelet-PCA, defined areas were generated in the central and peripheral regions. High-density zones presented considerable definition, while those of a lower density maintained a gradual transition. To complement the analysis presented in Table 3, the original set of EIM images used as the input for each of the methods evaluated is included in Figure 7. The set comprises seven layers corresponding to different tissue depths in the breast. These original images are presented to provide a visual context for the structural composition prior to the application of integration and fusion methods. When interpreting EIM images, the choice of the method can significantly influence the perception of their features. Some techniques may enhance the ability to identify critical information, while others may introduce barriers to proper interpretation. This distinction is vital, especially in the realm of automatic medical image analysis, where it can make a significant difference in achieving an opportune and accurate diagnosis. To assess the usefulness of these methods in the context of an image classification task, experiments were conducted using the different classifiers to identify the EIM images as causing low or high suspicion.