# MR Images, Brain Lesions, and Deep Learning
###### Abstract
There are three types of ischemic stroke according to the [PERSON] clinical classification system [24]: (1) partial anterior circulation syndrome (PACS), where the middle/anterior cerebral regions are affected; (2) lacunar anterior circulation syndrome (LACS), where  
Figure 1: Diseases considered in this review: (**a**) ischemic stroke, which occurs when a vessel in the brain is blocked; (**b**) demyelinating disease, which is the loss of the myelin layer in the axons of neurons; and (**c**) white matter hyperintensities (WMHs) of ischemic stroke and demyelination, as shown by the magnetic resonance imaging–fluid attenuated inversion recovery (MRI-FLAIR) modality, which shows that without an expert, it is difficult to distinguish one disease from another because of their similarities in WMHs. the occlusion is present in vessels that provide blood to the deep-brain regions; and (3) total anterior circulation stroke (TACS), when middle/anterior cerebral regions are affected due to a massive brain stroke [24, 25]. Ischemic stroke is a common cerebrovascular disease [1, 26, 27] and one of the principal causes of death and disability in low- and middle-income countries [1, 4, 6, 7, 27, 28, 29]. In developed countries, brain ischemia is responsible for 75-80% of strokes, and 10-15% are attributed to a hemorrhagic brain stroke [4, 25]. A demyelination disease is described as the loss of myelin with relative preservation of axons [8, 22, 29]. [PERSON] [22] notes that there are demyelinating diseases in which axonal degeneration occurs first and the degradation of myelin is secondary [7, 22]. Accurate diagnosis classifies the demyelinating diseases of the central nervous system (CNS) according to the pathogenesis into \"demyelination due to inflammatory processes, demyelination caused by developed metabolic disorders, viral demyelination, hypoxic-ischemic forms of demyelination and demyelination produced by focal compression\" [22]. The inflammatory demyelination of the CNS is the principal cause of the common neurological disorder multiple sclerosis (MS) [8, 19, 20, 22, 30], which affects the central nervous system [31] and is characterized by lesions produced in the white matter (WM) [32] of the brain and affects nearly 2.5 million people worldwide, especially young adults (ages 18-35 years) [4, 30, 31]. The detection, identification, classification, and diagnosis of stroke is often based on clinical decisions made using computed tomography (CT) and MRI [33]. Using MRI, it is possible to detect the presence of little infarcts and assess the presence of a stroke lesion in the superficial and deep regions of the brain with more accuracy. This is because the area of the stroke region is small and is clearly visible in MR images compared to CT [4, 21, 25, 26, 28, 34]. The delimitation of the area plays a fundamental role in the diagnosis since it is possible to misdiagnose stroke as other disorders [35, 36], e.g., glioma lesions and demyelinating diseases [19, 20]. For identifying neurological disorders like stroke and demyelinating disease, the manual segmentation and delineation of anomalous brain tissue is the gold standard for lesion identification. However, this method is very time consuming and specialist experience dependent [25, 37], and because of these limitations, automatic detection of neurological disorders is necessary, even though it is a complex task because of data variability, e.g., in the case of ischemic stroke lesions, data variability could include the lesion shape and location, and factors like symptom onset, occlusion site, and patient differences [38]. In the past few years, there has been considerable research in the field of machine learning (ML) and deep learning (DL) to create automatic or semiautomatic systems, algorithms, and methods that allow detection of lesions in the brain, such as tumors, MS, stroke, glioma, AD, etc. [4, 6, 8, 9, 10, 26, 28, 30, 36, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48].