# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### ML and DL Classifiers Applied to Diagnosis of Ischemia and Demyelinating Diseases  
In this subsection, we discuss the different classifiers that have been utilized in the literature. Additional details such as datasets and the measure metrics of the algorithms and the tasks are presented in Tables 2 and 5. Even though there are a large number of publications related to ischemic stroke (27 documents) and most deal with the classification of stroke patients versus normal controls, or the prediction of post-stroke functional impairment or treatment outcome [21; 25; 26; 28; 33; 38; 42; 66; 67; 72; 74; 75; 77; 78; 80; 167; 168; 179; 184; 185; 190; 193; 194; 195], there is a paucity of results related to demyelinating diseases alone. However, there are some publications dealing with multiple sclerosis (MS), which is the most common demyelinating disease (2) [191; 192]. In addition, there are articles related to WMHs (5) [19; 196; 20; 125; 19] as well as articles that combine ischemic stroke with MS and other brain injuries like gliomas (4) [36; 76; 79; 180]. Different studies [21; 72; 92; 189] related to stroke (see Table 5 and Figure 1) and its different types use, principally, classifiers of ML to determine the properties of the lesion. The classifiers most commonly used are the SVM and random forest (RF) [189]. According to [PERSON] et al. [189], the RF has some advantages over the SVM because the RF can be trained quickly and provides insight into the features that can predict the target outcome [189]; in addition, the RF can automatically perform the task of feature selection and provide a reliable feature importance estimate. Additionally, the SVM is effective only in cases where the number of samples is small compared to the number of features [92; 189]. Along similar lines, [PERSON] et al. [28] reported that the RF algorithm works better when one has a large dataset, and it is more robust when there are a higher number of trees in the decision-making process; they reported an accuracy of 93.4% and a DSC index of 0.94 in their study. [PERSON] et al. [72] presented results that predict ischemic tissue fate pixel by pixel based on multi-modal MRI data of acute stroke using a flexible support vector machine algorithm [72]. [PERSON] et al. [33] proposed an identification of ischemic stroke through the SVM with a linear kernel and cross-validation folder with an accuracy of 73% using a private dataset of 192 patient scans, while [PERSON] et al. [184] with a private dataset of 1000 patients for the same task used only the random forest (RF) classifier and obtained an accuracy of 95%. The combination of the traditional classifier like the SVM and RF with a CNN show better results. For example, [38, 72, 193] report values of the DSC between 0.80 and 0.86. [PERSON] and [PERSON] [167] reported that through a combination of kernelized fuzzy C-means clustering and an SVM, they achieved an accuracy of 98.8% and sensitivity of 99%. A method for detecting stroke presence using the SVM and feed-forward backpropagation neural network classifiers is presented in [21]. For extraction of the features of the segmentation of the stroke region, \(k\)-means clustering was used along with an adaptive neuro fuzzy inference system (ANFIS) classifier, since the other two methods failed to detect the stroke region in low-edge brain images, resulting in an accuracy and precision of 99.8% and 97.3%, respectively. The different developments in architectures of DL models contribute to better evaluation and segmentation results. For example, [PERSON] et al. [179] proposed a combination of U-Net and fractal networks. Fractal networks are based on the repetitive generation of self-similar objects and ruling out of residual connections [134, 179]. They reported on sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) using a public database (ISLES 2015, ISLES 2017), with an accuracy of 0.9908 and a DSC of 0.8993 for SPES and corresponding values of accuracy of 0.9914 and a DSC of 0.883 for SISS. [PERSON] et al.