## 4 Results  
We first compared the classification performances of the backbone networks, i.e., VGG-16, ResNet-50, DenseNet-201, and EfficientNetV2-L, using individual parametric images separately. Table 1 shows the results of the accuracy, recall, precision, F1-score, and AUC for various CNN models in terms of parametric images. The best scores of each performance metric are marked in bold for comparison. In this study, we primarily focused on comparing the classification performances in terms of individual parametric images rather than backbone models because the CNN models used in our experiment were not modified or optimized for BUS images. For the overall behavior, the entropy images exhibited the best classification performance in all network models, and the B-mode images showed the second-best results. Because the B-mode image was generated from an envelope image through log compression to reduce the dynamic range for human vision (as shown in Figure 2), the original information carried in the RF signals might be lost or distorted in the B-mode image. The entropy image, however, was directly calculated from an envelope image without log compression. Therefore, the reflective properties of the soft tissue were well-preserved for classification. The phase images showed the best performance in terms of recall scores for all backbone networks, while the accuracies were relatively low. Among TP, FP, TN, and FN, a \begin{table}
\begin{tabular}{c c c c c c} \hline
**Backbone** & **Image Type** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** & **AUC** \\ \hline \multirow{4}{*}{VGG-16} & B-mode & 75.5 & 87.43 & 73.96 & 80.13 & 0.8021 \\  & Entropy & **80.0** & 85.57 & **79.56** & **82.46** & **0.8487** \\  & Phase & 65.0 & **96.14** & 60.47 & 74.24 & 0.6368 \\  & Attenuation & 74.0 & 86.48 & 70.88 & 77.90 & 0.7673 \\ \hline \multirow{4}{*}{ResNet-50} & B-mode & 76.5 & 73.00 & **82.06** & 77.27 & 0.8058 \\  & Entropy & **77.0** & **82.62** & 76.07 & **79.21** & **0.8091** \\  & Phase & 71.5 & 77.05 & 72.30 & 74.60 & 0.7796 \\  & Attenuation & 70.5 & 74.76 & 73.17 & 73.96 & 0.7109 \\ \hline \multirow{4}{*}{DenseNet-201} & B-mode & 79.0 & 82.43 & 81.60 & 82.01 & 0.8677 \\  & Entropy & **82.0** & 81.86 & **83.60** & **82.72** & **0.8885** \\  & Phase & 74.5 & **89.38** & 70.45 & 78.79 & 0.7480 \\  & Attenuation & 74.5 & 82.62 & 72.79 & 77.39 & 0.7816 \\ \hline \multirow{4}{*}{EfficientNetV2-L} & B-mode & 74.5 & 84.62 & 72.61 & 78.15 & 0.7665 \\  & Entropy & **79.5** & 86.52 & **77.33** & **81.67** & **0.8205** \\ \cline{1-1}  & Phase & 69.5 & **88.52** & 65.72 & 75.44 & 0.6553 \\ \cline{1-1}  & Attenuation & 74.5 & 84.71 & 72.36 & 78.05 & 0.7610 \\ \hline \end{tabular} Note that the best scores of each performance metric are marked in bold for comparison.  
\end{table} 
Table 1: Classification performances of various CNNs using individual parametric images. relatively low FN resulted in a high recall score. Because the high FP and the low TN for the phase images produce low accuracy and precision scores, all performance metrics should be considered to evaluate the classification performance for each parametric image. Figure 6 shows the average scores of five-fold experiments using the VGG-16 backbone network. Owing to the extremely low FN score for phase images, the recall score was 96.14%. However, scores of accuracies and precision showed extremely low prediction results with high FP and low TN. Although phase images emphasize the boundaries of organs, the phase-only reconstruction technique with average magnitude, described in Section 2.2.2, requires further study to improve the classification performance for the use of phase images. To improve the classification performance, we experimented with the ensemble architecture, shown in Figure 5b, using multiple parametric images. Two voting methods, namely uniform and weighted soft voting, were applied in the decision layer, and the same performance metrics as in Table 1 were used.