# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 3 Proposed Methodology
### Datasets  
The proposed methodology is evaluated using three publicly available digital mammography datasets: MIAS, mini-DDSM, and CSAW-M.  
The Mammographic Image Analysis Society (MIAS) database [29] contains 322 digitized film mammograms with a resolution of 1024 \(\times\) 1024 pixels. Each image is annotated with breast density, lesion type (if present), and severity (normal, benign, or malignant). Lesion center coordinates and approximate radii are also provided. This dataset is widely used for early-stage algorithm validation due to its manageable size and well-defined labels.  
The mini-DDSM dataset [38] is a curated subset of the DDSM (Digital Database for Screening Mammography) dataset, composed of 9684 mammograms with improved contrast and reduced noise. Images are labeled as normal, benign, or malignant. The dataset includes breast density information and lesion contours when applicable. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & \multicolumn{2}{c}{**Advantages**} & **Disadvantages** & **References** \\ \hline \multirow{4}{*}{**Machine Learning**} & \(\bullet\) & Easy implementation & \(\bullet\) & Sensitive to preprocessing and classifier configuration & [9; 10; 11; 12; 13; 14] \\  & \(\bullet\) & Accurate results & \(\bullet\) & Requires a large amount of data & \\  & \(\bullet\) & Does not require data augmentation & \(\bullet\) & Sensitive to hyperparameter configuration & \\  & \(\bullet\) & Accurate results & \(\bullet\) & Requires GPU for training and inferior & [16; 17; 18; 19; 20; 32; 33; 34; 35; 36] \\ \multirow{4}{*}{**Deep Learning**} & \(\bullet\) & Sputry detection of multiple regions of interest (ROIs) & \(\bullet\) & Complex implementation & \\  & \(\bullet\) & Accurate results & \(\bullet\) & High computational cost & \\  & \(\bullet\) & Combines low- and high-level features from both machine learning & \(\bullet\) & Requires large datasets & [21; 22; 23; 25; 27; 28; 37] \\ \cline{1-1}  & & and deep learning & \(\bullet\) & Demands classifier optimization & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of principal approaches for breast cancer classification in mammography images.  
mini-DDSM provides a more diverse and balanced representation across classes compared with MIAS.  
The CSAW-Mammography dataset [39] includes 10,020 mammograms collected from routine clinical screenings in Sweden. Images are captured using full-field digital mammography and labeled as either malignant or normal based on biopsy-confirmed diagnoses. The dataset covers a wide range of patient ages and breast densities, providing realistic clinical variability. No lesion localization is provided, making it suitable for weakly supervised classification tasks.  
In total, 20,026 images are considered for the development and evaluation of the proposed methodology. It is worth mentioning that the CSAW-M dataset comprises a significantly larger number of images (10,020) compared with MIAS and mini-DDSM, which could introduce a bias in the combined dataset. To mitigate this effect in the binary classification task (malignant vs. benign), we ensured that class balance was preserved during training and evaluation. Specifically, the positive and negative class proportions were maintained consistently across all datasets.