# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Performance Comparisons with Existing Methods

Table 9: Comparison of classification performance with state-of-the-art methodologies. “RepVal” indicates whether repetitions on validation were reported. is noteworthy that the number of images represents only the 4.2% of the total number of images utilized in our proposed methodology. [PERSON] [14] utilized 1923 images from three combined datasets: MIAS, DDSM, and INbreast. In this approach, multiple ROIs are extracted from each image using a bidimensional empirical mode decomposition (BEMD) algorithm. For feature extraction, the GLCM and LBP algorithms were used. In contrast to our approach, the feature vectors are classified using SVM without any dimensionality reduction. Using a k-fold cross-validation, this work reports for the DDSM dataset 98.62% accuracy, 98.65% specificity, 98.60% sensitivity, and an AUC of 0.9818. For the MIAS dataset, it reports 98.04% accuracy, 98.31% specificity, 98.12% sensitivity, and 0.9817 AUC. Finally, for the INbreast dataset, the reported values are 98.26% accuracy, 98.21% specificity, 97.60% sensitivity, and 0.9823 AUC. Notice that these high-performance results were obtained using only 12.6% of the total images considered in the proposed method, which limits a proper evaluation of its generality. In [PERSON] [13], a total of 633 images were considered for the development of the model. A single ROI per image is extracted, which is enhanced by the CLAHE algorithm. Dimensionality reduction is performed using CFS, while classification is carried out using SVM. Additionally, a training-testing-validation of 60-20-20 is considered without number of repetitions. This method reports 96.87% of accuracy, 97.23% of sensitivity, 95.47% of specificity, and 0.98 of AUC. It should be noted that the number of considered images is very small compared with that utilized in the development of the proposed methodology. [PERSON] [27] uses 797 images from MIAS, INbreast, and WDBC datasets. A hybrid methodology is utilized that combines CNN features with a machine learning approach. Similar to our approach, the mammograms are preprocessed to reduce noise and artifacts. Feature extraction is performed using the ResNet18 model. Dimensionality reduction is carried out using PSO, DFO, and CSO algorithms. The feature vectors are classified using the weighted KNN algorithm. For performance evaluation, a 75/25 train-test ratio and 5-fold cross-validation was considered. This approach achieved an accuracy of 84.35% on the MIAS dataset, 83.19% on the INbreast dataset, and 97.36% on the WDBC dataset. It is noteworthy that, despite employing evolutionary algorithms with high computational complexity for dimensionality reduction, the performance improvement is moderate. In contrast, the proposed approach utilizes a simple statistical approach for feature reduction, obtaining similar performance. The proposed methodology introduces a preprocessing stage to remove artifacts from digital mammograms, followed by a monogenic signal-based technique for image representation and enhancement of delicate breast tissue to improve feature extraction. A lightweight CNN-based model for feature extraction was constructed using EfficientNetV2 variants, without applying data augmentation. Dimensionality reduction is performed using simple statistical measures. High classification performance for breast cancer detection is achieved through ensemble classifiers, demonstrating the effectiveness of the proposed feature extraction strategy across several well-known datasets. By performing 100 trials for each experiment, we ensured statistically reliable results, providing robust validation of the evaluation methodology. The consistent performance of the proposed method on substantially larger datasets (Table 9) and its effective feature extraction model, straightforward dimensionality reduction, absence of data augmentation, and rigorous statistical validation through repeated trials make it a more robust, generalizable, and clinically applicable alternative to existing approaches, which are often developed using limited datasets.