## 3 Results  
In our experiment, to evaluate the efficacy of the derived shape features, classification tests were carried out on the 5252 breast ultrasound images, from which 94 attributes were extracted for each image's region of interest (ROI). Subsequently, feature selection was performed through an exploration of various subsets of features, assessing each to identify the most effective combination. This process resulted in a refined set of 43 features, selected from the original pool using a correlation-based feature selection approach coupled with a best-first search strategy. In this study, the computational experiments were conducted on a workstation equipped with a 2.93 GHz Intel Xeon CPU, 3 GB of RAM (Intel, Santa Clara, CA, USA), and running the Windows 7 OS. The k-fold cross-validation technique [39] was utilized on the entire database to determine the classification accuracy of CAD system. In this study, with k set to 10, the adopted cases were randomly partitioned into 10 sets according to the pathological result. For each round of experiments, nine sets are regarded as the training set and the remaining one as the test set, i.e., 80% of the cases were used as training sets, and 20% were testing sets. In each round of experiments, 15% of the training sets were used as the validation dataset. The model's performance was observed based on the validation dataset to adjust the model's learning rate and determine when to stop training to avoiding overfitting the model. Finally, the performance of the CAD system was evaluated by averaging the ten experiments. While pathological results served as the gold standard, the performance of the experimental result could be quantified using five key metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) (Table 3). \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Indices** & **Formula** \\ \hline Accuracy & (TP + TN)/(TP + TN + FP + FN) \\ Sensitivity & TP/(TP + FN) \\ Specificity & TN/(TN + FP) \\ Positive Predictive Value (PPV) & TP/(TP + FP) \\ Negative Predictive Value (NPV) & TN/(TN + FN) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of Five Designated Performance Metrics. In our study, the performance of the proposed features compared with other shapes feature sets: the spatial gray level dependence (SGLD) feature (F1), depth-width ratio feature (F2), number of depressions feature (F3), and the orientation feature (F4), across five metrics to assess the effectiveness of the newly developed features (Figure 9). The outcomes summarized in Table 4 indicate that the proposed features stand out with the highest accuracy at 92.91% compared to other feature sets, which follow with an accuracy of 88.42% (F1), 89.32% (F2), 89.52% (F3), and 58.29% (F4), respectively. In terms of sensitivity and specificity, the proposed features outperform all other feature sets with a rate of 89.94% and 91.38%, respectively, indicating the ability to correctly identify positive and negative cases. Moreover, the proposed features set shows strong results in PPV (90.29%), and NPV (91.45%). Hence, the findings reveal that the introduced features enhance the classification of breast tumors with greater precision across the five designated performance metrics. The correlation between sensitivity and specificity is depicted through the receiver operating characteristic (ROC) curve, which plots the true positive (TP) rate versus the false positive (FP) rate. The ROC curve offers a sophisticated tool for examining a diagnostic method's classification precision. The area under the curve (AUC) defines the ROC curve's form, making the AUC a valuable indicator of testing precision (Figure 10). An AUC value of 1 signifies an ideal test, while an AUC of 0.5 indicates a test without diagnostic value.