# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 2 Literature Review
### Machine Learning-Based Methods  
Classical machine learning methods have been extensively investigated for breast cancer classification in digital mammograms due to their good results. Table 1 summarizes recent ML-based methods considering variations in feature extraction, dimensionality reduction, and classifiers.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Reference** & **Images** & **Data Balanced?** & **No. of Classes** & **Train–Test Split (\%)** & **Testing Results (\%)** \\ \hline [PERSON] [9] & 319, 1500 & No & 3 & NR & 97.49 (Acc, MIAS) \\
92.61 (Acc, DDSM) & 319, 1500 & No & 2 & NR & 100 (Acc, MIAS) \\ \hline [PERSON] [10] & 326, 1500, 410 & No & 2 & NR & 98.94 (Acc, DDSM) \\  & & & & & 98.76 (Acc, INbreast) \\ \hline [PERSON] [11] & 322, 569 & No & 2 & NR & 100.00 (Acc, MIAS) \\  & & & & & 91.13 (Acc, WBCD) \\ \hline [PERSON] [12] & 651 & No & 2 & 70/30 & 97.85 (Acc, DDSM) \\ \hline [PERSON] [13] & 633 & No & 2 & 60/20/20 & 96.34 (Acc, CESM) \\ \hline [PERSON] [14] & 1923 & No & 2 & NR & 98.62 (Acc, DDSM) \\  & & & & & 98.04 (Acc, MIAS) \\  & & & & & 98.26 (Acc, INbreast) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reported results of recent machine learning-based methods for breast cancer classification. “NR” = Not reported; other acronyms are listed in the acronym list.  
One method employs cross-diagonal texture matrix (CDTM) and Haralick features from non-overlapping ROI blocks in mammograms. Feature optimization is performed using kernel principal component analysis (KPCA) and the grasshopper optimization algorithm (GOA). This method reports classification accuracies of 97.49% for MIAS [9; 29] and 92.61% for DDSM [30].  
Another approach utilizes the fast discrete curvelet transform with wrapping (FDCT-WRP) for feature extraction. Dimensionality reduction is performed using principal component analysis (PCA) and linear discriminant analysis (LDA). Classification is carried out using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). The results report accuracies of 100%, 98.94%, and 98.76% on the MIAS, DDSM, and INbreast datasets, respectively [10].  
A different method applies the discrete Chebyshev transform (DCT) combined with the contrast-limited adaptive histogram equalization (CLAHE). Feature selection is carried out using KPCA and differential evolution (DE) [11]. This approach reports near 100% precision on the MIAS dataset and 91.13% on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset.  
Feature extraction based on the crow search algorithm (CSA) and Harris Hawks optimization (HHO) has also been explored. The extracted features are classified using artificial neural networks (ANNs) and support vector machines (SVMs), achieving 97.85% accuracy on the DDSM dataset with a 70/30 train-test split [12; 31].  
In addition, a proposed computer-aided diagnosis (CAD) system designed for contrast-enhanced spectral mammography (CESM) utilizes morphological and texture features. These features are reduced using correlation-based feature selection (CFS) and classified by SVM with a sigmoid kernel, reporting an accuracy of 96.87%, sensitivity of 97.23%, specificity of 95.47%, and an area under curve (AUC) of 0.98 [13].  
A multi-ROI method based on k-means clustering and bidimensional empirical mode decomposition (BEMD) extracts texture features using the gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). Classification is performed using SVM with a radial basis function (RBF) kernel, achieving accuracies of 98.04%, 98.26%, and 98.62% on the MIAS, DDSM, and INbreast datasets, respectively [14].  
In summary, classical ML methods demonstrate good performance on various mammography datasets, performing feature extraction combined with traditional classifiers. However, their dependence on manual ROI selection, ad hoc feature extraction methods, and dataset-specific tuning may limit scalability and generalization in broader clinical applications.