## 5 Discussion  
The performance of deep learning approaches is generally determined by the architecture of a deep neural network, as well as the quality of the training dataset. In medical US applications, there are few well-annotated datasets available in terms of both varieties (clinical cases, US systems, and institutions) and the amount of data. The classification performance of the proposed method did not exhibit significant difference than the conventional method, because each channel-CNN model was not trained enough for its feature (entropy and phase information) due to small size of training data used. Although entropy and phase images represent distinct features of an image, a sufficient number of training datasets are needed to extract each feature since both images are calculated directly from the same B-mode image. However, the proposed FC-CNN method provided better performances than the traditional method for all CNN models under the same conditions, and the proposed method would be helpful for breast ultrasound classifications when each feature-channel CNN model is well trained with large datasets. In this study, we proposed one possible way for increasing the number of training data for two feature maps in order to extract (or abstract) its own feature more exactly. Since two feature maps were calculated from a B-mode image, the extended feature maps could be generated using various sizes of filters or different signal processing techniques. For the entropy images, three more sizes of entropy filters which are 9 \(\times\) 9, 15 \(\times\) 15, and 31 \(\times\) 31 pixels, in addition to the entropy filter of 25 \(\times\) 25 pixels, were applied to calculate new entropy images in the training dataset. Since a larger entropy filter relatively smooths local variations of complex structure inside soft tissue, it provides a different view of the tumor area comparing to the background tissue. For the phase images, two additional phase images were reconstructed using one dimensional FFT in either axial or lateral directions with their average magnitudes, and one phase image was obtained using a magnitude of Gaussian distribution. Because the horizontal and vertical edges in a B-mode image are emphasized using axial and lateral 1-D FFT respectively, these phase images also provide additional information of the tissue scanned. Figure 6 shows the examples of extended entropy and phase images. Experimental results for the ResNet-34 model exhibited that classification accuracy with larger extended feature maps outperformed 3.8% higher than that of original feature maps. This shows that final accuracy of the proposed architecture could be improved as the performance of each feature channel improved with larger feature maps. To compensate the limitations of dataset including the size as well as class imbalance, data augmentation is commonly used to increase the amount of data for the learning process. However, overfitting is a common disadvantage inherited because the data augmentation artificially inflates datasets by image transformations and synthetic creations from a small set of original images. In the proposed architecture, two feature maps, entropy and phase images, along with a B-mode image, represent distinct characteristics that are not easily seen in the original image. Therefore, the proposed method can be considered as \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multicolumn{2}{c}{**Traditional CNN**} & \multicolumn{2}{c}{**Simple Voting**} & \multicolumn{2}{c}{**Weighted Voting**} & \multicolumn{2}{c}{**Simple Averaging**} & \multicolumn{2}{c}{**Weighted Averaging**} \\ \hline \multirow{2}{*}{ResNet-34} & Accuracy & 0.915 & 0.939 & 0.929 & 0.935 & 0.928 \\ \cline{2-7}  & STD & 0.016 & 0.026 & 0.023 & 0.026 & 0.016 \\ \hline \multirow{2}{*}{DenseNet-161} & Accuracy & 0.923 & 0.937 & 0.936 & 0.932 & 0.929 \\ \cline{2-7}  & STD & 0.020 & 0.029 & 0.023 & 0.033 & 0.033 \\ \hline \hline \end{tabular}
\end{table}