## 3 Materials and Methods
### CNN Models  
In this section, the CNN architectures considered for the classification task of the shape-based breast lesions are briefly introduced. * VGG  
The VGG [50] comes in two famous versions, with 16 and 19 layers comprising 144 million parameters. This study considers the earlier VGG-16, which consists of several number of channels, 3 \(\times\) 3 receptive fields, and a stride of 1. This model is composed of convolution layers, max pooling layers, fully connected layers with 5 blocks and each block with a max pooling layer, and extra convolutional layers contained in the last three blocks. * ResNet  
The deep neural networks suffer from the gradient vanish problem, which led to the development of residual network (ResNet) architecture. The ResNet takes care of the gradient vanishing problem and makes sure the performance remains satisfactory over the top and lower layers. ResNet comes with several variants where the number of layers is the distinguishing parameter among numerous architectures, however the underlying mechanism remains similar. This architecture utilizes skip connections between layers. The ResNet-34 and ResNet-50 [51] contain 34 and 50 layers and implement residual learning. This net is efficient to train and also improves the accuracy, which led us to utilize the two versions of network for multiclass classification purposes. * ResNeXt The ResNeXt, a counterpart of ResNet, is a specifically designed image classification network with very few tuneable parameters. It contains a series of blocks with a set of aggregations of similar topology with an additional dimension called cardinality. This cardinality, which creates major difference between its brother networks, competes with the depth and width of the network [52]. The simpler architecture based on VGG and ResNet with fewer parameters yields better accuracy on ImageNet classification dataset. The word _NeXt_ in the name of the network refers to next dimension which surpasses ResNet-101, ResNet-152, ResNet-200, Inception-v3, and ResNet-v2 on the ImageNet dataset in accuracy. * DenseNet The DenseNet [53], or in other cases, dense convolutional network, is a type of CNN designed to guarantee the maximum information flow between all layers in the network. The layers are subjected to align the feature map size and connect among each other, forming a dense network. The DenseNet works on feed-forward principle. Each layer in the network receives the input from the preceding layer, grabs the additional input, and hands it over to the following layer along with the feature map. All the layers follow a similar analogy. Differently from ResNets, in which the features are not combined through summation before they are passed into a layer, the feature combination is performed by the concatenation of these ones. DenseNet comes with several variants where the number of layers is the distinguishing parameter among numerous architectures, however the underlying mechanism remains similar. The DenseNet-121 and DenseNet-161 contain 121 and 161 layers and follow the feed-forward method. This net is efficient to train and also improves the accuracy, which led us to utilize the two versions of network for multiclass classification purposes. * SqueezeNet The SqueezeNet is another popular CNN model particularly known for its smaller size. The major motivations and reasons that caused this network to be smaller include the following: (a) during the procedure of training, the communication over the servers is shortened, (b) the minimum requirement of bandwidth for exporting a model from cloud to any other device is also cut, and (c) the smaller a model is, the less hardware and memory it requires to run. The SqueezeNet architecture is also simple; it contains 8 fire modules sandwiched between two convolutional layers. The sandwiched fire modules also contain a squeeze convolution layer with numerous filters of varying sizes.