## 3 Experimental Results  
In the proposed classification scheme, for the classical feature-based classification approach, the classification performances are investigated on B-mode US image, parametric (P) image, Contourlet transformed image, parametric version of Contourlet transformed image, weighted parametric version of Contourlet transformed image, etc. The results are shown in Table 6, where it is evident that the application of statistical modeling and Contourlet transform improves the accuracy of the classification. Here, it can be seen that the features extracted from the B-mode image provide the least amount of accuracy. For B-mode images without any statistical modeling or Contourlet transform applied on them, the highest accuracies obtained for database-I, database-II, and database-III were 92%, 92.05%, and 92.15%, respectively, all of them obtained using the KNN classifier. Applying Nakagami and RiIG statistical modeling on the B-mode images improves the accuracies of the classification, the highest being 93.5%, 93.25%, and 92.55% for databases I, II, and III, respectively, obtained from the SVM classifier. Applying Contourlet transform on the B-mode images also proved to be effective in increasing the accuracies, the highest being 93%, 92.65%, and 93.05%, for databases I, II, and III, respectively, using the SVM classifier  
Figure 9: The proposed CNN architecture. From the results, it is seen that applying either technique on the B-mode images improves the classification performance. In the case of CP images, where both the techniques are applied together, the highest classification accuracies increased to 93%, 93.15%, and 93.55% for databases I, II, and III, respectively, all of them obtained from the SVM classifier. In the case of our proposed WCP images, the highest accuracies increased further to 97.5%, 97.55%, and 97.95% for databases I, II, and III, respectively, and all were obtained from the SVM classifier. Here, it could be easily seen that the RiIG statistical model provided better performance for all seven classifiers compared to the Nakagami statistical model for all types of images in database-I, database-II, and database-III, proving RiIG to be more suitable for the statistical modeling of the B-mode images. As it was shown that the RiIG modeled WCP images provided the best result for the feature engineering method, the CNN method was applied on RiIG modeled WCP images only. From the results, it could be easily seen that CNN-based feature extraction provided more accuracy than algorithm-based feature extraction. For the CNN-based approach, the highest accuracies obtained for databases I, II and III were 98.05%, 98.35%, and 98.55%, respectively, all of them obtained from the KNN classifier. From Table 6, it is evident that the proposed RiIG based WCP image is the most suitable choice for the classification of breast tumors in both the feature extraction-based approach and CNN-based approach. Moreover, the CNN-based approach provides higher accuracy over the feature extraction-based approach. The confusion matrices of 10-fold cross-validation result for the proposed CNN-based approach employing the KNN classifier are shown in Table 7 along with performance indices such as accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) by measuring true positive (TP), true negative (TN), false positive (FP), and false-negative (FN), where positive stands for malignant tumor and negative stands for a benign tumor. It is observed that for all three databases, the values of accuracy, sensitivity, specificity, PPV, and NPV are greater than 98%. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Accuracy (\%) with Database-I**} \\
**Classifier** & **B-Mode** & \begin{tabular}{c} **B-Mode Parametric** \\ **Nakagami** \\ \end{tabular}  & **RiIG** & \begin{tabular}{c} **Contourlet** \\ **Nakagami** \\ \end{tabular}  & **Contourlet** & \ \begin{tabular}{c} **Contourlet Parametric (CP)** \\ **Nakagami** \\ \end{tabular}