# A Grey Wolf-Based Method for Mammographic Mass Classification
## 4 Experimental Results
### Results and Discussion
From the above three sub-scenarios, it can be noticed that the BGWO algorithm worked very well with the fused features where it has a big impact of the features reduction (i.e., 298 out of 970 were selected to give the highest results). The BGWO as a feature selector with fused curvelet and wavelet features gave the best results in terms of accuracy, precision, recall and ROC area among all the results of other features i.e., wavelet and curvelet individually. This means that if the BGWO algorithm is used as a feature selection technique, only 30.7% of the total mammogram features can be used to detect whether a mammogram image is normal or abnormal. \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 & 56 & 98.0\% & 98.0\% & 98.0\% & 0.995 \\ Feat. set  
When comparing the results in Table 2 and in Tables 3-5, i.e., without and with using BGWO as a feature selection technique, respectively, it can be noticed that the use of BGWO has improved the results of all scenarios. In case of using the curvelet only, the results have improved by 0.5% for all evaluation measures while the needed features have been reduced from 810 to 275. In case of using the wavelet only, the results have also improved by 0.7% for accuracy, precision and recall and by 0.001 for the ROC area while the used features were reduced from 160 to 46 (this is a big improvement in terms of performance). For the last scenarios, i.e, when using fused curvelet and wavelet features, the results of using BGWO showed an improvement of 1.0% for accuracy, precision and recall and giving the same results (1.00) for the ROC area. This was achieved while only using 298 out of 970 features. Thus, it can be claimed that using BGWO could improve the detection of abnormal mammogram cases 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). Using BGWO for benign and malignant cases: This scenario aims to classify mammogram images as benign and malignant directly, i.e., helping in the cancer diagnosis process. In other words, it investigates which set of features would give high classification (benign and malignant) performance. 1. When the curvelet features (810 features) were given to the BGWO algorithm, the best result was obtained by Set 16 with a number of features 305, accuracy 77%, precision 77%, recall 77% and ROC area 0.87, see Table 6.
2. When the wavelet features, 160 features, were fed to the BGWO algorithm, the results showed that Set 4 gave the best results with the number of features being 67, accuracy 77.5%, precision 77.7%, recall 77.5% and ROC area 0.891, see Table 7.
3. When the fused features of both curvelet and wavelet features, 970 features, were given to the BGWO algorithm, the results showed that Set 6 gave the best results with the number of features being 374, accuracy 78%, precision 78.1%, recall 78% and ROC area 0.871, see Table 8.  
\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 & 279 & 96.8\% & 96.8\% & 93.6\% & 0.994 \\ Feat. set 2 & 270 & 98.5\% & 98.5\% & 97.0\% & 0.997 \\ Feat. set 3 & 350 & 98.0\% & 98.0\% & 96.0\% & 0.998 \\ Feat. set 4 & 3  
From these three sub-scenarios, it can be noticed that the BGWO performed very well with the fused features, where it has a significant impact of the feature reduction (374 was selected from 970 features). Simultaneously, the results of the fused-based features were the best results in terms of accuracy, precision, recall and ROC area among all the results of other scenarios i.e., when using wavelet and curvelet individually, see Tables 6-8.