# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### Machine Learning and Deep Learning
#### 3.1.1 Machine Learning Methods  
Machine learning (ML) can be considered as a subfield of artificial intelligence (AI). [PERSON] and [PERSON] (1988) and [PERSON] et al. (2018) state that the main aim of ML is to develop mathematical models and computational algorithms with the ability to solve problems by learning from experiences without or with the minimum possible human intervention; in other words, the model created will be able to be trained to produce useful outputs when fed input data ([PERSON] et al., 2018). [PERSON] et al. (2018) state that recent studies demonstrate that machine learning algorithms give accurate results for the determination of study protocols for both brain and body MRI. Machine learning can be classified into (1) supervised learning methods (e.g., support vector machine, decision tree, logistic regression, linear regression, naive Bayes, and random forest) and (2) unsupervised learning methods (K-means, mean shift, affinity propagation, hierarchical clustering, and Gaussian mixture modeling) ([PERSON] et al., 2018) (Figure 9). _Support vector machine (SVM)_: This is an algorithm used to classify and perform regression and clustering. An SVM is driven by a linear function similar to logistic regression ([PERSON] et al., 2018) but with the difference that the SVM only outputs class identities and does not provide probabilities. \[W^{T}x+b \tag{3}\]  
An SVM classifies between two classes by constructing a hyperplane in high-dimensional feature space ([PERSON] et al., 2018). The class identities are positive or negative when Equation (3) is positive or negative, respectively. For the optimal separation of the hyperplane between classes, the SVM uses different kernels (dot products) ([PERSON] et al., 2018; [PERSON] et al., 2018). More information and details of SVMs are given in the literature ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). _k-Nearest neighbor (k-NN)_: The _k_-NN is a non-parametric algorithm (it means no assumption about the underlying data distribution) and can be used for classification or regression ([PERSON] et al., 2018; [PERSON] et al., 2018). The _k_-NN is based on the measure of the Euclidean distance (distance function) and a voting function in \(k\) nearest neighbors ([PERSON] et al., 2018), given \(N\) training vectors. The value of \(k\) (the number of nearest neighbors) decides the classification of the points between classes. The _k_-NN has the following basic steps: (1) calculate the distance, (2) find the closest neighbors, and (3) vote for labels ([PERSON] et al., 2018). More details of the _k_-NN algorithm can be found in references ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). Programming libraries such as Scikit-Learn have algorithms for the _k_-NN ([PERSON] et al., 2018). The _k_-NN has higher accuracy and stability for MRI data but is relatively slow in terms of computational time ([PERSON] et al., 2018). As an aside, it is interesting to note that the nearest-neighbor formulation might have been first described by the Islamic polymath [PERSON] in his famous book _Kitab al Manazir_ (The Book of Optics, ([PERSON] et al., 2018)) over a 1000 years ago. _Random forest (RF)_: This technique is a collection of classification and regression trees ([PERSON] et al., 2018). Here, a forest of classification trees is generated, where each tree is grown on a bootstrap sample of the data ([PERSON] et al., 2018). In that way, the RF classifier consists of a collection of binary classifiers where each decision tree casts a unit vote for the most popular class label (see Figure 9d) ([PERSON] et al., 2018). More information is given elsewhere ([PERSON] et al., 2018). _k-Means clustering (k-means):_ The _k_-means clustering algorithm is used for segmentation in medical imaging due to its relatively low computational complexity ([PERSON] et al., 2018; [PERSON] et al., 2018) and minimum computation time ([PERSON] et al., 2018). It is an unsupervised algorithm based on the concept of clustering.