## 4 Results  
We compared the classification performances of the traditional CNN (with B-mode only) and the proposed FC-CNN (with multiple time-domain feature maps) architecture for nine backbone networks including variations of VGGs, ReNets, and DenseNets. For the performance metrics of accuracy, recall, precision, F1 score, and AUC, all simulation results for classification are shown in Table 2. Note that the highest performance for each metric is represented in bold. Although we used four different combining algorithms in the decision layer to produce the final prediction, the results of the proposed FC-CNN method shown in Table 2 were obtained using the simple voting algorithm, which achieved the best performance. A detailed comparison among the combining strategies is presented in the following section. While the performance among individual backbone networks made little difference, the proposed FC-CNN generally achieved better performance than the traditional CNN in most performance metrics. As for the accuracy, ResNet backbones showed higher accuracies for the traditional CCN and DenseNet backbones for the FC-CCN architecture, but the differences were not significant. For the performance metrics of recall and precision, we hardly found a trend or tendency according to the backbone networks, but most simulation results of the FC-CNNs were better than those of the traditional CNNs. To compare the advantages of the proposed FC-CNN architecture in detail, we chose the ResNet-34 network and DenseNet-161 network cases, which achieved the best performance in all aspects. Figure 4 shows the classification accuracies for the traditional CNN with B-mode images and the proposed FC-CNN with multiple time-domain feature maps. The simulation results of the traditional CNN with entropy image and phase image are also plotted for reference. The accuracies for the CNN with a single image (i.e., B-mode, entropy, and phase images separately) are lower than those of the FC-CNN with three feature images simultaneously for both ResNet-34 and DenseNet-161 network models. Since the accuracy of B-mode image is better than those of entropy and phase image for the traditional CNN with a single image, it could be said that B-mode images represent morphological as well as textual information of the tissue scanned. However, the proposed FC-CNN exhibits better performance than the traditional CNN with a B-mode image. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **ACC (\%)** & **Recall (\%)** & **Precision (\%)** & **F1 Score (\%)** & **AUC** \\ \hline \multirow{7}{*}{Traditional CNN} & VGG-11 & 89.28 & 87.45 & 87.93 & 87.67 & 0.9427 \\  & VGG-13 & 89.30 & 88.57 & 88.31 & 88.34 & 0.9468 \\  & VGG-16 & 87.97 & 87.43 & 86.28 & 86.62 & 0.9275 \\  & ResNet-18 & 90.07 & 89.95 & 88.56 & 89.07 & 0.9576 \\  & ResNet-34 & 91.50 & 90.73 & **91.29** & 90.85 & 0.9577 \\  & ResNet-50 & 91.24 & 90.64 & 90.99 & 90.79 & 0.9563 \\  & DenseNet-121 & 90.85 & 90.30 & 89.83 & 90.06 & 0.9587 \\  & DenseNet-161 & **92.29** & **92.21** & 91.01 & **91.56** & **0.9685** \\  & DenseNet-169 & 90.72 & 90.85 & 89.28 & 90.02 & 0.9582 \\ \hline \multirow{7}{*}{Proposed FC-CNN} & VGG-11 & 91.76 & 89.34 & 89.70 & 89.38 & 0.9572 \\  & VGG-13 & 91.50 & 89.52 & 89.33 & 89.32 & 0.9562 \\  & VGG-16 & 90.33 & 89.22 & 88.35 & 88.59 & 0.9487 \\  & ResNet-18 & 93.07 & 91.31 & 91.37 & 91.28 & 0.9631 \\  & ResNet-34 & **93.86** & 92.03 & **92.76** & 92.31 & 0.9645 \\  & ResNet-50 & 93.07 & 92.28 & 92.02 & 92.15 & 0.9678 \\  & DenseNet-121 & 92.55 & 91.72 & 91.23 & 91.43 & 0.9645 \\  & DenseNet-161 & 93.73 & **93.19** & 92.40 & **92.76** & **0.9706** \\  & DenseNet-169 & 93.33 & 92.78 & 92.44 & 92.57 & 0.9676 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances of traditional CNN and the proposed FC-CNN for backbone networks.