## 3 Results  
In this study, we have used the OASBUD dataset [26]. This dataset involves 52 malignant and 48 benign images. Each lesion of the OASBUD dataset consists of two scans, one is longitudinal and other is transverse. A second dataset of BUS images collected at BHE [27] included 210 malignant and 437 benign lesions is also employed. The segmented part in the breast lesion exposes cancer affected portion. This malignant region is required to be extracted from the background tissues. Watershed transformation algorithm based on the marker function and minima imposition is used for the segmentation of the BUS images. The hybrid feature set of GLCM and shape-based were used to assess our model for correct classification of breast lesion. The outcomes are concisely reviewed based on the classification performance and the corresponding results based on longitudinal and transver \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature set** & **Size** & **Description** \\ \hline GLCM & \(1\times 20\) & \
\begin{tabular}{c} Produce texture features based on second order \\ statistical method by co-occurrence matrix \\ \end{tabular} \\ \hline Shape-based & \(1\times 7\) &
\begin{tabular}{c} Compute elongation, compactness, rectangularity, \\ solidity, roundness, eccentricity, and convexity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Employed features description. Figure 7: Confusion matrix [8]. scan of the OASBUD dataset are given in Tables 5 and 6, respectively. While the results of BUS images collected at BHE are reported in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline Ensemble (Preset: Ensemble method: RUSBoost, RUSBoosted trees, learner type: Decision tree, number of learners: 30, maximum number of splits: 20, learning rate: 0.1) & \\ \hline \hline \multirow{8}{*}{Transverse} & Accuracy & 95.00\% & 88.72\% to 98.36\% \\  & Sensitivity & 92.73\% & 82.41\% to 97.98\% \\ \cline{1-1}  & Specificity & 97.78\% & 88.23\% to 99.94\% \\ \cline{1-1}  & PPV & 98.08\% & 88.00\% to 99.72\% \\ \cline{1-1}  & NPV & 91.67\% & 81.05\% to 96.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance based on hybrid feature vector using transvers scan of OASBUD dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Scan**} & **Statistic** & **Value** & **95\% of Confidence Interval** \\ \hline \hline \multirow{8}{*}{Longitudinal} & Accuracy & 97.00\% & 91.48\% to 99.38\% \\  & Sensitivity & 96.23\% & 87.02\% to 99.54\% \\ \cline{1-1}  & Specificity & 97.87\% & 88.71\% to 99.95\% \\ \cline{1-1}  & PPV & 98.08\% & 88.00\% to 99.72\% \\ \cline{1-1}  & NPV & 95.83\% & 85.51\% to 98.90\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Scan**} & **Statistic** & **Value** & **95\% of Confidence Interval** \\ \hline \hline \multirow{8}{*}{Transverse} & Accuracy & 95.00\% & 88.72\% to 98.36\% \\  & Sensitivity & 92.73\% & 82.41\% to 97.98\% \\ \cline{1-1}  & Specificity & 97.78\% & 88.23\% to 99.94\% \\ \cline{1-1}  & PPV & 98.08\% & 88.00\% to 99.72\% \\ \cline{1-1}  & NPV & 91.67\% & 81.05\% to 96.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance based on hybrid feature vector using longitudinal scan of OASBUD dataset. As shown in Tables 5-7, all statistical measures of diagnostic are estimates and presented with confidence intervals [41]. The 97% accuracy with CI of 91.48% to 99.38% is achieved through the proposed system using hybrid features when used with ensemble classifier for longitudinal scan of the OASBUD dataset is presented in Table 5. It illustrates the impact of hybrid features for correct classification of lesion. Furthermore, high values of specificity (97.87% with CI of 88.71% to 99.95%) and sensitivity (96.23% with CI of 87.02% to 99.54%) specify that the hybridization of GLCM and shape-based features has successfully managed to indicate a more distinct judgment between the binary sets, such as benign and malignant lesions.