## 2 Related Work  
Image integration is used in the computational analysis of medical images, mainly when information is distributed between different depth levels. Methods such as the Coefficient of Variation (CV), the Root Mean Square Contrast (\(C_{\text{RMS}}\)), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Gaussian pyramid fusion, and Wavelet-PCA fusion have been successfully explored for integrating medical images [17,18]. According to [19], applying the \(C_{\text{RMS}}\) over color SONAR images to combine information from different chromatic channels resulted in significant improvements in the perception of details. On the other hand, the use of Gaussian pyramid fusion allows for the integration of multiscale information while preserving relevant details at different resolution levels, making it effective for hyperspectral image classification [20]. Regarding mammography, this method has been used to improve the segmentation and detection of relevant anatomic structures [17].  
Methods such as CLAHE, in combination with the CV, enhance the local contrast and highlight areas of interest in medical images [18,21]. Wavelet-PCA has been utilized to fuse high-resolution images with detailed spectral data, thereby enhancing the visual quality and classification in medical applications [22]. In particular, for fusing images obtained using computed tomography and magnetic resonance imaging, it has optimized the differentiation of relevant anatomic structures and identified pathological tissues [23].  
Hybrid approaches combining techniques such as the weighted average and Brovey Transform have enabled an increase in the quality of fusion images, resulting in improvements in the PSNR (signal-to-peak noise ratio) and mean quadratic error [24]. For the binary classification of medical images, integration is crucial for enhancing the precision and sensitivity of models. Recently, the combination of the Wavelet Transform and PCA has enabled discriminative features to be highlighted that facilitate the differentiation between classes, achieving significant improvements in the classification of breast lesions [23].  
Previous studies have highlighted the advantages of image integration and fusion in medical images from diverse modalities. Recent advances in multimodal data fusion have demonstrated significant potential in enhancing clinical diagnostics and prognostics by integrating heterogeneous medical data sources. [PERSON] et al. [25] introduced a dual-input CNN that processes raw audio signals and STFT spectrograms to classify pediatric respiratory sounds, achieving state-of-the-art performance. In critical care scenarios, [PERSON] et al. [26] combined chest X-rays, respiratory sound-derived features, and routine ICU parameters using deep learning and Logistic Regression, resulting in an AUC rate of 0.759, substantially outperforming single-modality models. Moreover, [PERSON] et al. [27] reported that image-to-image fusion is the most prevalent strategy and that most methods leverage deep or machine learning to combine data modalities. However, this has not been explored in the case of IEM images. These approaches could help integrate EIM images, where the detection of subtle variations in the conductivity is fundamental for identifying abnormalities. In automatic classification, information integration has been shown to enhance the precision and robustness of classification models.