# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 2 Review of the State-of-the-Art
### CNNs in Image Classification  
Image classification in the field of CV is a significant task that has been researched for several years [30; 31]. It is used as a primary task in different application areas including event detection [32], scene understanding [33], and object tracking [34]. In terms of human accuracy [35], machine learning is the most promising technique compared to other available approaches [31; 36]. As deep learning developed, CNNs were introduced as a new state-of-the-art concept in image classification [30; 35; 37]. This type of network can overcome several challenging issues in image classification such as occlusion, deformation, background clutter, and changes in scale and viewpoint. The most interesting part of CNNs is that the feature extractor and the classifier are put together. However, traditional machine learning methods have two separate steps: the first step is the handcrafted techniques for feature extraction; the second step is when extracted features are used to train the classifier such as K-nearest neighbor (KNN) [36] and support vector machine (SVM) [38]. Another benefit of CNNs is that they can work with binary or multi-class classification. CNNs have shown extraordinary achievements in several pattern recognition and CV tasks [30; 37] and have solved many problems in computer vision.  
[PERSON] et al. [30] developed the CNN further when they introduced the AlexNet network. Subsequently, several architectures were introduced after the achievements of AlexNet, such as VGG-Net [37], GoogLeNet [39], and ResNet [35]. Due to the success of these models, the majority of recently proposed CNNs is often based on them and enhances performance by adding extra convolutional layers [37; 39]. In general, for classification tasks, CNN's architecture involves several convolutional layers (at the beginning) and fully connected layers (on the top) heaped one over the other. These CNNs extract features via the convolutional layers and executes the classification tasks by the fully connected layers [37; 40; 41].  
The number of layers, or depth of the CNN, plays a critical role in a superior classification model as its learning capacity is controlled by changing its depth [42]. Examining the proposed models in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) showed that accuracy is increased as the model becomes deeper [30; 35; 37]. Thus, as the depth is enlarged, accuracy is enhanced up to saturation level [35]. Afterward, increasing the depth (via stacking extra layers) will not enable the CNN model to reduce the error [35]. As an alternative to the usual architecture of stacked layers, ResNet introduced a less complicated structure with up to 152 layers (deeper network), while GoogLeNet introduced a hierarchical structure of convolutional layers for classification.  
A significant fact is to distinguish a well-behaved architecture while considering learning forms and model depth. The chosen CNN architecture must also be able to generalize the dataset, free from overfitting and the proper learning ability for the existing dataset. Several attempts were considered in classifying undersized datasets using CNN [43] and although these techniques significantly enhanced performance, they faced the problem of overfitting due to direct training from scratch. Therefore, the question of shallow architectures having sufficient ability to capture whole features for undersized data cannot be answered. How deep must the CNN architecture be to train with undersized data? Current CNN models avert direct training and enhance performance using TL techniques. TL helps to address the problem of lack of training data.