# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 5 Conclusions  
Digital mammography is one of the best diagnostic tools for breast cancer. In general, the detection of breast cancer using digital mammography can achieve a sensitivity of 86.9% and a specificity of 88.9% [58]. High-quality mammograms and experienced radiologists are fundamental for breast cancer detection [59; 60; 61]. Accurate interpretation of mammograms is crucial for minimizing false positives, consequently reducing patient anxiety [62] and the need for frequent mammograms [63]. This work presents a methodology for classifying mammography images into cancer and non-cancer categories by extracting features using EfficientNetV2 convolutional neural network models. The processing of mammograms, along with the use of phase congruency, local phase, and local orientation, is an efficient approach to highlight important details of breast tissue for feature extraction. Dimensionality reduction of feature descriptors using simple statistics increased the overall efficiency of the proposed methodology without sacrificing classification performance for different ensemble classifiers. By combining the MIAS, DDSM, and CSAW-M datasets, a total of 15, 506 images were considered for the development of the proposed method. Using EfficientNetV2S and a stacking classifier, a performance of 86.28% accuracy, 78.75% precision, 86.14% recall, and 80.09% F1-score was obtained from the three combined datasets. By considering only the CSAW-M dataset and utilizing EfficientNetV2 XL and the stacking classifier, a performance of 93.47% accuracy, 87.61% precision, 93.19% recall, and 90.32% F1-score was obtained, confirming the effectiveness of the proposed approach for feature extraction. A limitation of the proposed method is its design for only two categories: malignant (cancer) and benign, which can potentially restrict its usability for a broader diagnosis. Furthermore, although the proposed method was trained on a limited database with some unbalanced categories, it demonstrates strong performance in breast cancer detection. These limitations could be addressed by incorporating newer image databases for training and considering additional classification categories. For future work, we will explore state-of-the-art deep learning techniques and employ advanced hyperparameter optimization to develop an advanced CNN for feature extraction and explore advanced classification ensembles that are more suitable for multiclass breast cancer classification. This work concludes that it is necessary to construct methodologies with a sufficient number of images from different datasets and to define consistent schemes for comparing methodologies, such as the number of classes, images per dataset, training/testing split ratio, number of experiment repetitions, and performance measures. Conceptualization, E.O.M.M. ; methodology, E.O.M.M. ; software, E.O.M.M. ; validation, V.H.D.-R.; formal analysis, V.H.D.-R.; investigation, E.O.M.M. and V.H.D.-R.; data curation, E.O.M.M. ; writing--original draft preparation, E.O.M.M. ; writing--review and editing, V.H.D.-R.; visualization, E.O.M.M. ; funding acquisition, V.H.D.-R. All authors have read and agreed to the published version of the manuscript. This research was funded by Instituto Politecnico Nacional, Secretaria de Investigacion y Posgrado, through project SIP20253728. * **Infrared Consent Statement:** Not applicable. * **Data Availability Statement:** Publicly available datasets were analyzed in this study.