## 5 Discussion  
While a set of RF data obtained from a single patient produces various parametric images representing the distinct characteristics of the scanned tissue, no single parametric or B-mode image can provide an outstanding result for the classification task. Therefore, ensemble architecture is significant for improving classification performance. One of the key factors for obtaining better prediction results is the optimal selection of a CNN model for each parametric image. For example, as shown in Table 1, the DenseNet-201 model exhibited the best accuracy in B-mode images, whereas the VGG-16 model exhibited the best accuracy in entropy images. Because the size of our dataset is diminutive, it is difficult to determine the best CNN model for parametric images in our experiment. However, different CNN models for a specific parametric image might show better performance because each parametric image represents the distinct characteristics of the scanned tissue. Therefore, the ensemble architecture with optimal CNN models for various parametric images could be the next deep learning method for ultrasound analysis.  
The ensemble deep learning approaches using multiple inputs have been widely utilized to improve the overall prediction performance in various applications. Spatial and frequency domain models, including three-channel color images and one-hot encoded DCT coefficients, were applied the CNN architecture for steganography classification [44]. Ensemble deep learning architecture using a deep belief network was proposed for forecasting the load demand time series and exhibited better performance [45]. The proposed end-to-end ensemble architecture using multiple parametric images in time and spectral domains could be extended to the 1-D time series classification for individual RF signals. This modification not only provides another aspect of the quantitative ultrasound analysis for representing echogenicity along the propagation path, but also generates abundant data for training of deep neural network to prevent overfitting problems.  
Another factor for improving the classification performance in an ensemble architecture is the voting algorithm for each prediction result of a parametric image. Hard voting (also known as majority voting) is simple and easy to implement. However, the final prediction is acceptable only when the individual classifier results are relatively comparable. In our experiment, because the accuracies of the B-mode and entropy images were higher than those of the phase and attenuation images, hard voting did not provide better classification performance owing to an imbalance of accuracies. Uniform or weighted soft voting methods are suitable for the classification of BUS, and further studies are required to determine the optimal weights for the combination of parametric images and CNN models. If larger datasets are available, these voting weights are updated in the training process to improve the classification performance of the proposed ensemble architecture.  
For the parametric images, the entropy and phase images could be used for data augmentation in a broad sense and utilized by the ensemble CNN architecture as one of the multiple inputs. While ultrasound RF signals for BUS are hardly available, various B-mode images are relatively easy to access from several datasets. The entropy and phase images primarily represent the textual and morphological properties, respectively. Therefore, they can be generated from B-mode images. As explained in the previous section, while B-mode images are visually optimized for humans through log compression, the entropy and phase images from a B-mode image provide meaningful information in the time domain for the classification task. Attenuation images, however, cannot be estimated from a B-mode image. Therefore, further effort is required to collect medical RF datasets in the future to improve the rich analysis of ultrasound information carried by original signals.