# A Grey Wolf-Based Method for Mammographic Mass Classification
## 4 Experimental Results
### Results and Discussion
This means that when the BGWO algorithm was used as a feature selection technique, only 38.5% of total features can be used to diagnose the mammogram images as benign or malignant with 0.871 ROC area results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Selected Features** & **Number of Features** & **Accuracy** & **Precision** & **Recall** & **ROC Area** \\ \hline Feat. set 1 & 313 & 71.5\% & 71.5\% & 71.5\% & 0.854 \\ Feat. set 2 & 254 & 72.5\% & 72.6\% & 72.5\% & 0.851 \\ Feat. set 3 & 275 & 72.5\% & 72.7\% & 72.5\% & 0.863 \\ Feat. set 4 & 331 & 73.5\% & 73.7\% & 73.5\% & 0.875 \\ Feat. set 5 & 279 & 72.5\% & 72.7\% & 72.5\% & 0.861 \\ Feat. set 6 & 346 & 72.5\% & 72.6\% & 72.5\% & 0.855 \\ Feat. set 7 & 268 & 69.0\% & 69.3\% & 69.0\% & 0.847 \\ Feat. set 8 & 287 & 72.5\% & 72.9\% & 72.5\% & 0.857 \\ Feat. set 9 & 266 & 72.0\% & 72.1\% & 72.0\% & 0.861 \\ Feat. set 10 & 259 & 72.5\% & 72.5\% & 72.5\% & 0.856 \\ Feat. set 11 & 266 & 72.5\% & 72.7\% & 72.5\% & 0.857 \\ Feat. set 12 & 305 & 72.0\% & 72.0\% & 72.0\% & 0.855 \\ Feat. set 13 & 315 & 70.5\% & 70.5\% & 70.5\% & 0.855 \\ Feat. set 14 & 232 & 74.5\% & 74.7\% & 74.5\% & 0.858 \\ Feat. set 15 & 246 & 71.0\% & 71.4\% & 71.0\% & 0.85 \\
**Feat. set 16** & **305** & **77.0\%** & **77.0\%** & **77.0\%** & **0.87** \\ Feat. set 17 & 315 & 74.5\% & 74.7\% & 74.5\% & 0.859 \\ Feat. set 18 & 336 & 72.5\% & 72.5\% & 72.5\% & 0.863 \\ Feat. set 19 & 268 & 72.0\% & 72.3\% & 72.0\% & 0.862 \\ Feat. set 20 & 305 & 76.0\% & 76.0\% & 76.0\% & 0.872 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detection of benign or malignant using BGWO with curvelet features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Selected Features** & **Number of Features** & **Accuracy** & **Precision** & **Recall** & **ROC Area** \\ \hline Feat. set 1 & 67 & 75.0\% & 75.3\% & 75.0\% & 0.868 \\ Feat. set 2 & 74 & 72.0\% & 72.2\% & 72.0\% & 0.857 \\ Feat. set 3 & 60 & 74.5\% & 74.9\% & 74.5\% & 0.872 \\
**Feat. set 4** & **67** & **77.5\%** & **77.7\%** & **77.5\%** & **0.891** \\ Feat. set 5 & 56 & 77.0\% & 77.4\% & 77.0\% & 0.868 \\ Feat. set 6 & 55 & 74.5\% & 74.8\% & 74.5\% & 0.875 \\ Feat. set 7 & 70 & 74.5\% & 74.7\% & 74.5\% & 0.867 \\ Feat. set 8 & 71 & 74.0\% & 74.6\% & 74.0\% & 0.864 \\ Feat. set 9 & 54 & 74.5\% & 74.9\% & 74.5\% & 0.863 \\ Feat. set 10 & 44 & 76.0\% & 76.3\% & 76.0\% & 0.869 \\ Feat. set 11 & 61 & 75.5\% & 75.9\% & 75.5\% & 0.868 \\ Feat. set 12 & 60 When comparing the results in Table 2, without using BGWO, and in Tables 6-8, i.e., with using BGWO as a feature selection technique for diagnosing mammogram images as benign or malignant, it can be noticed that the use of BGWO has improved the results of all scenarios. In the case of using the curvelet only, the results have improved by nearly 3.0% for evaluation measures: accuracy, precision and recall and by 0.007 for the ROC area. This improvement comes with also a reduction in the needed features, i.e., 305 out of 810. In case of using the wavelet only, the results have also improved by nearly 4.5% for accuracy, precision and recall and by 0.018 for the ROC area, while 67 out of 160 features were needed. For the last scenario, when using fused curvelet and wavelet features, there was a better improvement where the results of using BGWO showed an improvement of around 4.0% for accuracy, precision and recall. Simultaneously, giving the same results (0.871) for the ROC area. This was achieved while only using 374 out of 970 features. Thus, it can be claimed that using BGWO could improve the diagnosis of benign and malignant mammogram cases from the raw images with no processing except extracting the ROI. This improvement comes in terms of the processing requirements (i.e., using small size of features) and the performance requirements (i.e., accuracy, precision, recall and the ROC area).