## 4 Results and Discussion  
The experimental validation of the AOBNN-BDNN method is tested using the breast ultrasound image dataset [26]. It contains a total of 780 images with three class labels, as shown in Table 1.  
Figure 2: Flowchart of AO technique. Figure 3 exhibits the confusion matrices created by the AOBNN-BDNN algorithm on the applied data. On the entire dataset, the AOBNN-BDNN method has recognized 429 samples as benign, 210 samples as malignant, and 132 samples as normal. Simultaneously, on 70% of training (TR) data, the AOBNN-BDNN technique has recognized 311 samples as benign, 135 samples as malignant, and 92 samples as normal. Concurrently, on 30% of testing (TS) data, the AOBNN-BDNN method has recognized 118 samples as benign, 75 samples as malignant, and 40 samples as normal. \begin{table}
\begin{tabular}{l l} \hline \hline
**Class** & **No. of Images** \\ \hline Benign & 437 \\ Malignant & 210 \\ Normal & 133 \\
**Total Number of Images** & **780** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Figure 3: Confusion matrices of AOBNN-BDNN approach: (**a**) entire dataset, (**b**) 70% of TR data, and (**c**) 30% of TS data. Table 2 and Figure 4 portray a brief classification outcome of the AOBNN-BDNN method on the entire dataset. The results implied that the AOBNN-BDNN method has effectually recognized all three classes. For instance, the AOBNN-BDNN model has identified benign class samples with \(accu_{y}\) of 98.85%, \(prec_{n}\) of 99.77%, \(reca_{l}\) of 98.17%, \(F_{score}\) of 98.96%, and MCC of 97.68%. Moreover, the AOBNN-BDNN algorithm has identified Malignant class samples with \(accu_{y}\) of 98.97%, \(prec_{n}\) of 96.33%, \(reca_{l}\) of 100%, \(F_{score}\) of 98.13%, and MCC of 97.46%. Furthermore, the AOBNN-BDNN technique has identified normal class samples with \(accu_{y}\) of 99.87%, \(prec_{n}\) of 100%, \(reca_{l}\) of 99.25%, \(F_{score}\) of 99.62%, and MCC of 99.55%. Table 3 and Figure 5 display a detailed classification outcome of the AOBNN-BDNN algorithm on 70% of TR data. The results denoted the AOBNN-BDNN methodology has effectually recognized all three classes. For example, the AOBNN-BDNN technique has identified benign class samples with \(accu_{y}\) of 98.53%, \(prec_{n}\) of 99.68%, \(reca_{l}\) of 97.80%, \(F_{score}\) of 98.73%, and MCC of 97.02%. Additionally, the AOBNN-BDNN approach has identified Malignant class samples with \(accu_{y}\) of 98.72%, \(prec_{n}\) of 95.07%, \(reca_{l}\) of 100%, \(F_{score}\) of 97.47%, and MCC of 96.67%. Besides, the AOBNN-BDNN technique has identified normal class samples with \(accu_{y}\) of 99.82%, \(prec_{n}\) of 100%, \(reca_{l}\) of 98.92%, \(F_{score}\) of 99.46%, and MCC of 99.35%. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Entire Dataset** & & & & & \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **MCC** \\ \hline Benign & 98.85 & 99.77 & 98.17 & 98.96 & 97.68 \\ Malignant & 98.97 & 96.33 & 100.00 & 98.13 & 97.46 \\ Normal & 99.87 & 100.00 & 99.25 & 99.62 & 99.55 \\
**Average** & **99.23** & **98.70** & **99.14** & **98.91** & **98.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of the AOBNN-BDNN approach with different measures under the entire dataset. Figure 4: Result analysis of AOBNN-BDNN approach under entire dataset. Table 4 and Figure 6 exhibit brief classification results of the AOBNN-BDNN technique on 30% of the TS dataset. The outcomes implied that the AOBNN-BDNN approach has effectually recognized all three classes. For example, the AOBNN-BDNN methodology has identified benign class samples with \(accu_{y}\) of 99.57%, \(prec_{n}\) of 100%, \(reca_{l}\) of 99.16%, \(F_{score}\) of 99.58%, and MCC of 99.15%. along with that, the AOBNN-BDNN algorithm has identified Malignant class samples with \(accu_{y}\) of 99.57%, \(prec_{n}\) of 98.68%, \(reca_{l}\) of 100%, \(F_{score}\) of 99.34%, and MCC of 99.03%.