## 4 Results
Utilizing metrics such as the accuracy and F1-score allowed us to assess the effectiveness of the predictions while also taking the class imbalance into account, an essential factor in medical data. Moreover, metrics like the recall and precision provided critical insights into the positive classification of the samples. Before presenting the classification results, we will describe a series of preliminary experiments that were conducted to determine the most suitable CNN for feature extraction. Three widely used architectures were tested: VGG19, VGG16, and ResNet. Although VGG16 required a lower computational cost, it slightly underperformed in terms of capturing the subtle structures present in EIM images. ResNet, on the other hand, exhibited unstable behavior and high variability across the metrics, likely due to its residual architecture not adapting well to the particular texture and contrast of medical images. VGG19 emerged as the most effective and reliable extractor. Its deeper structure allowed it to capture more complex spatial patterns and subtle variations in the tissue density. Across the board, classification models using features extracted with VGG19 achieved higher  
Figure 7: The original sample of EIM images consisting of seven layers used as the input for the integration and fusion methods, whose results are presented in Table 3.  
accuracy and F1-scores, along with lower standard deviations. Based on these findings, the main experiments proceeded using VGG19, ensuring a more stable and robust analysis of the classification of EIM images. To prevent overfitting during classification using VGG19 caused by potentially correlated features, an assessment of the multicollinearity was conducted. To verify this, the Variance Inflation Factor (VIF) was computed for the features extracted by VGG19, of which 14 produced valid VIF scores with values ranging between 1 and 5 and only 1 slightly exceeded 5 (VIF \(\approx\) 5.27), indicating low-to-moderate correlations. These results confirm that multicollinearity was not a limiting factor in the performance or interpretability of the logistic regression classifier used in this study [47; 48]. Table 4 shows the classification results obtained with the use of the integration methods. Overall, the CV achieved the best performance when used with different classifiers, particularly Logistic Regression. However, the best performance in terms of the precision and recall was achieved by CVCLAHE with the same classifier. Finally, the CRMS showed the lowest results with most classifiers. On the other hand, the results of the fusion methods (Table 5) stand out, suggesting these were the most efficient approaches. Notably, the best results across the different metrics were achieved with Random Forest and XGBoost. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Integration Methods**} & \multicolumn{2}{c}{**Fusion Methods**} \\ \hline CV & CVCLAHE & CRMS & CLAHE\({}_{F}\) & Gaussian Pyramid & Weighted Avg. & Wavelet-PCA \\ \hline
**B1** & & & & & & \\
**B2** & & & & & & \\
**B3** & & & & & & \\
**B4** & & & & & & \\
**B5** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Visual comparison of the integration and fusion methods applied to the EIM images. Each row (B1–B5) represents a different BI-RADS grade. According to the color scale, red tones indicate regions of higher electrical conductivity (denser fibroglandular tissue), while blue tones represent areas of lower conductivity (fatty tissue). gistic Regression to achieve performance levels comparable to--or even surpassing--those reported using more complex approaches. These findings expand upon the contributions made in earlier work by [PERSON] et al. [9; 16], where EIM images were classified using a multiclass BI-EIM labeling scheme (levels 1 to 5). In their first study [9], various feature selection techniques and classifiers such as SVM and k-NN were explored, achieving a maximum accuracy of 76.0% with raw, unnormalized images using k-NN.