# A Grey Wolf-Based Method for Mammographic Mass Classification
## 5 Conclusions  
The extracted features plays an essential role in mammographic image classification. In addition, the features selection could improve the detection performance and decrease the computational complexity of the feature set. This paper investigates the impact of employing the BGWO algorithm on statistical features extracted using decomposed curvelet and wavelet sub-bands from the ROI. The BGWO is a feature selection algorithm aiming to choose the best set of features giving high performance. Experiments were conducted without and with applying the BGWO using the public dataset, DDSM. The results of these experiments are assessed in terms of accuracy, precision, recall and ROC area employing random forests classifier using 10-fold cross validation. From the designed scenarios, the results showed that the use of the BGWO algorithm as a feature selection technique has improved the computational complexity for feature vector construction. The best results were obtained when the fusion of both curvelet and wavelet was used where only 30.7% of total features can be used to detect whether the ROI is normal tissue or abnormal (tumor) with ROC area reaching 1.0. In case of diagnosing the mammogram images as benign or malignant, the results showed that the BGWO algorithm could help to use only 38.5% of total features to diagnose that a given mammogram is benign or malignant without prior checking whether it is normal or abnormal. The results showed that this could be achieved with a high ROC area result at 0.871. In the future, it is planned to investigate whether the diagnosis results can be improved by using different types of features as it was not high as the case of the detection results.  
Conceptualization, M.M.E. and T.G.; methodology, M.T. and M.M.E.; software, M.M.E., M.T. and E.E.M.; validation, A.A.A., M.A.A. and E.E.M.; investigation, T.G. and A.A.A.; resources, M.T. and M.M.E.; writing--original draft preparation, M.T., M.M.E. and T.G.; writing--review and editing, A.A.A., M.A.A. and E.E.M.; supervision, T.G. and M.A.A.; project administration, M.M.E.; funding acquisition, E.E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Taif University Researchers Supporting Project number (TURSP-2020/20), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Classification** & \multicolumn{2}{c}{**Feature**} & \multicolumn{2}{c}{**Number**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**ROC Area**} \\
**Problem** & **Selection Method** & & & & & \\ \hline \multirow{4}{*}{Normal vs. Abnormal} & Chi-Squared [32] & 512 & 95.8\% & 95.9\% & 95.8\% & 0.996 \\  & Information Gain [33] & 383 & 96.25\% & 96.3\% & 96.3\% & 0.996 \\  & Significant attribute [34] & 512 & 99.0\% & 99.0\% & 99.0\% & 1.0 \\  & Correlation attributes [35] & 627 & 94.0\% & 94.1\% & 94.0\% & 0.989 \\  & **Proposed Method** & **298** & **100\%** & **100\%** & **100\%** & **1.0** \\ \hline \multirow{4}{*}{Benign vs. Malignant} & Chi-Squared [32] & 249 & 76.5\% & 76.6\% & 76.5\% & 0.867 \\  & Information Gain [33] & 250 & 76.0\% & 76.0\% & 76.0\% & 0.871 \\ \cline{1-1}  & Significant attributes [34] & 249 & 75.5\% & 75.6\% & 75.5\% & 0.868 \\ \cline{1-1}  & Correlation attributes [35] & 706 & 74.0\% & 74.1\% & 74.0\% & 0.863 \\ \cline{1-1}  & **Proposed Method** & **374** & **78.0\%** & **78.1\%** & **78.0\%** & **0.871** \\ \hline \hline \end{tabular}
\end{table}
Table 9: The comparison study between the proposed method and some famous feature selection methods.