Electrical Impedance Mammography (EIM) is a non-invasive method for evaluating breast tissue by generating conductivity maps across multiple depth levels. This article focuses on Gaussian Pyramid (GP) fusion, a multiscale fusion technique used to combine EIM images into a single and more representative image. By preserving both local and global features at different scales, this approach is applied to a binary classification scenario in the context of early breast cancer detection. The theoretical foundations, implementation, and results using a clinical dataset are presented. Among the classifiers evaluated through cross-validation with various k-fold configurations (k = 3, 5, 7, and 10), the SVM model achieved the best performance using 10-fold cross-validation, obtaining an accuracy of 84.21% and a precision of 95.07%. These results highlight the potential of this fusion technique as a viable alternative for automated analysis of EIM images, particularly in scenarios where preserving both global structure and fine tissue details is important.