# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 1 Introduction  
Neurological disorders affect as many as a billion people worldwide [1], with Ischemic stroke and Alzheimer's disease and other dementias being ranked the second and fifth significant causes of death, respectively. White Matter Hyperintensities (WMHs) are lesions localized in the periventricular and deep white matter [2; 3; 4; 5]. They are a hallmark of brain lesions associated with vascular and neuroinflammatory conditions, including ischemic damage, demyelination, and axonal loss [5; 6]. Ischemic stroke accounts for 87% of all strokes and occurs due to an obstruction or blockage of a vessel of small caliber (10 to 400 \(\upmu\)m) that irrigates the brain. There are two categories of stroke: ischemic and hemorrhagic [6]. Hemorrhic strokes are caused by bleeding into brain tissue when a blood vessel bursts [7; 8]. According to [9], cerebral ischemia has a temporal continuum that begins with hyperacute symptoms and progresses through acute, subacute, and chronic stages. Meanwhile, Demyelination disorders are associated with the WMHs that cause destruction or loss of the myelin sheath and loss of myelin-supporting cells such as oligodendrocytes and Schwann cells in the central and peripheral nervous system [10]. Myelin is a laminated membrane that concentrically and repeatedly surrounds axons with a radius of approximately 12 nm; it insulates peripheral nerves, nerves in the brain, spinal cord, and eyes [11] and it allows nerves to send signals or conduct electrical impulses typically and efficiently. The damage of myelin compromises neural transmission and could cause neurological deficits, such as vision changes, weakness, altered sensation, and cognitive problems [12], which are also the principal causes of multiple sclerosis (MS) [13]. Medical imaging represents one fundamental role in identifying, diagnosing, and monitoring brain lesions, helping clinicians distinguish between different types of lesions associated with various conditions. Particularly, WMHs can be seen through Magnetic Resonance Imaging (MRI), which allows the identification and quantification of microvascular lesions and silent ischemic strokes [14]. Among different MRI modalities, FLAIR (Fluid Attenuated Inversion Recovery) imaging [5; 15; 16] constitutes one of the most effective in detecting small WMH lesions related to ischemia and demyelination [17; 18; 19; 20]. However, accurately distinguishing these two types of lesions is a complex task, especially for an inexperienced radiologist, due to overlapping intensities and morphological variations in the FLAIR MR scans. In this sense, the traditional methods of lesion detection, segmentation, classification, or manual annotations in medical images have issues due to the high observer variability, many time-consuming tasks, interpreting, and limited generalization across patient datasets [20; 21]. These techniques often fail to capture small or irregularly shaped lesions, especially in conditions like ischemia and demyelination [22]. Research in artificial intelligence (AI) and its methods of image processing, like deep learning (DL) applied to medicine, constitutes one of the crucial tools for helping to get better and quick diagnosis, treatment, and prognosis of diseases [23]. Particularly concerning brain disorders, it allows optimization of the time-consuming tasks of detecting, segmenting, classification, lesion localization, analyzing complex brain imaging data, and generalizing better [20]. Moreover, the adaptability of DL models through fine-tuning and transfer learning enables robust performance even in datasets with limited annotations [24]. However, despite the advancements in AI for medical imaging, few studies have focused on distinguishing ischemic stroke-related WMHs from demyelination-related WMHs using deep learning (DL) methods, principally because detecting small and subtle WMH lesions presents a significant challenge, especially in automated image analysis.