### Deep Learning in Image Analysis  
In the domain of medical imaging, deep learning is mainly implemented by convolutional neural networks (CNNs) [25; 31; 32; 33], which are considered an efficient and dynamic approach for extracting features concerning image classification and segmentation tasks. Before CNN development, this process had to be accomplished using insufficient machine learning models or by hand. However, after their entrance into medical imaging, CNNs could use these features that had been learned directly from the data. Among the marked characteristics of CNNs is their capability of analyzing and classifying images, making them powerful deep learning models for image analysis [24; 25; 32; 34]. CNN resembles a standard artificial neural network (ANN) in its characteristics, utilizing backpropagation and gradient descent for training. However, more pooling layers and layers of convolutions are present concerning its structure.  
Among the most common CNNs methods in medical image analysis, there are:  
AlexNet (2012): Developed by [PERSON] et al. [35], this network has a similar architecture to LeNet [36] but consists of more filters per layer, including stacked convolutional layers. Its specifications include \(11\times 11\), \(5\times 5\), \(3\times 3\) convolutions, max pooling, dropout,data augmentation, rectified linear unit (ReLU) activations and stochastic gradient descent (SGD) with momentum [37]. It attaches ReLU activations after each layer, either convolutional or fully connected. The deep learning boom was attributed to AlexNet, when this architecture won the 2012 ILSVRC competition by a considerable margin. Some features worth mentioning are the computational split among many GPUs, dropout regularization, data augmentation, and the ReLU activation function.  
ZFNet (2013): This network constitutes a minor adaptation of AlexNet and won the 2013 ILSVRC competition [38].  
VGGNet16 (2014): This network incorporates 16 convolutional layers and is popular due to its consistent structure [39]. Being similar to AlexNet, this model presents only 3 \(\times\) 3 convolutions but comprises many filters. Currently, VGGNet16 is the most preferred choice in the community for feature extraction from images. At the same time, it gave great popularity to the notion of creating deeper networks by using smaller filter kernels [40].  
GoogleNet: It involves a standard piled convolutional layer and one or more fully connected layers [41]. Inception modules were also introduced, applying different-sized filters to the input and concatenated in the end. This way, the module can extract different levels of features simultaneously. Another notion introduced by GoogleNet, which won the 2014 ILSVRC competition, was that there is a global average pooling instead of fully connected layers in the network's ending, reducing the model parameters.  
ResNet (2015): This type of CNN architecture introduced the \"identity shortcut connection\" to handle the well-known \"vanishing gradients\" issue that characterizes deep networks. This technique revealed that extremely deep networks could use standard SGD along with residual modules for their training [42].  
DenseNet (2017): Being another important CNN architecture, DenseNet outweighs other networks as it sorted out the gradient vanishment problem by directly connecting all its layers. Meanwhile, feature delivery is optimized; therefore, it can make more efficient use of it. It is a widespread technique for disease diagnosis, and more recently, it efficiently addressed the task of cardiac disease classification, as reported in [43].