## 3 Methods
### Network Architecture  
The overall architecture of the traditional and proposed CNN is shown in Figure 3. The traditional CNN for the classification task, shown in Figure 3a, takes B-mode images (256 \(\times\) 256) as inputs to train the backbone network, and the output vector (1 \(\times\) 1000) of the backbone network is applied to the linear layer to obtain the final prediction. Various deep neural networks could be used as a backbone network, and the VGG, ResNet, and DenseNet were used in this study to provide reference performance for the BUS classification. The proposed architecture for BUS classification using multiple time-domain feature maps is shown in Figure 3b. This framework consists of the feature extraction stage and the decision layer along with a backbone network, and we call this architecture a feature-channel convolutional neural network (FC-CNN). The feature extraction stage makes two feature maps, entropy and phase images, from a B-mode image, and adjusts all three images with a size of 256 \(\times\) 256 pixels as input data for the backbone network. For the backbone networks, three separate CNN models were utilized for each feature-channel image. Each CNN model was independently trained by its own images (i.e., B-mode, entropy, and phase images) and the best model was saved when the highest accuracy occurs. Since entropy and phase images represent distinct characteristics of an image, it would be better to select the most appropriate CNN model for each feature channel respectively for the improvement of performances. However, since the main purpose of this work is to show the benefits of multiple time-domain feature maps derived directly from a B-mode image, the experiments were performed with single CNN model for all feature channels under the same conditions. The decision layer determines the final prediction for a patient after combining three individual prediction results for B-mode, entropy, and phase images. In this study, to determine a final decision by combining three outputs of each feature-channel, four different combining strategies are utilized that are simple voting, weighted voting, simple averaging, and weighted averaging. Because the output of an individual channel before the activation function consists of three probabilities corresponding to each class, we can use either probabilities or local prediction of each channel for the final classification result. While the simple voting algorithm makes the final prediction by the majority of classes for each channel output, the weighted voting algorithm sets double weight to the channel which has the highest probability in a feature vector. The simple averaging algorithm calculates the mean probability for all three classes first, and then determines the final decision with these mean probabilities. The weighted average algorithm applies the channel accuracy, which was obtained in the training phase, as a weight to the mean probability of each class. The classification performances among combining strategies are compared in the next section. In this study, several variations of VGG, ResNet, and DenseNet were used as backbone networks to compare the classification performances of the traditional CNN and the proposed FC-CNN. The VGG network is a classical CNN-based deep neural network proposed by the Visual Geometry Group, a research team at Oxford University [29]. For comparison purposes, we utilized relatively simple networks such as VGG-11, VGG-13, and VGG-16, which have fewer layers, because US images contain fewer morphological details than natural images. While the VGG network can extract more detailed information using the smallest filter of 3 \(\times\) 3 and more features of the data by stacking layers deeply, of about 16 and 19 layers, it also experiences the vanishing gradient problem as the layers become deeper [30]. The ResNet proposed by [PERSON] et al. [31] overcomes the vanishing and exploding gradient problems for deeper networks by the initialization or normalization method [32].