## 1 Introduction  
White matter (WM) hyperintensities or white matter lesions (WMLs) are a common occurrence in a variety of brain pathologies, including infection, issues in the body's immune system, small vessel ischemia, exposure to hazardous chemicals, and more. In many cases, the reason for the development of WMLs is unknown. These lesions show abnormal intensity signals on magnetic resonance imaging (MRI) such as T1-weighted (T1) and fluid-attenuated inversion recovery (FLAIR) MRIs. These WMLs are generally more prevalent in the MRIs of old-age people [1] and accumulating evidence has shown their association with various old-age diseases such as Alzheimer, cognitive deficit, cerebrovascular disease, and other psychiatric disorders [2; 3; 4; 5; 6; 7; 8]. Therefore, segmentation and quantification of WMLs is essential to gain an adequate understanding of the relationship between WMLs and old-age disorders. The quantification of WML load may have diagnostic and prognostic values for individual patients and may lead to personalized medicine for these patients [9]. There is certainly an immediate need to develop computerized methods for segmentation of WMLs in clinical radiology and to develop methods for detecting longitudinal changes in WMLs over the period of time [9; 10]. The presence of new lesions in a patient's brain MRI that already has WMLs will prompt the neurologist to consider a change in the regimen of disease-modifying medication. We also note that WML load in specific regions may have both diagnostic and prognostic value in dementia. The classical segmentation of WMLs is based on manual labeling, which is a time-consuming and laborious process: It has higher inter- and intra-observer variability, and heavily depends on the experience level of the person segmenting the image. Therefore, it is highly advantageous to develop an automatic, reproducible, and faster WML segmentation method. This has been an active area of research, and several automatic segmentation methods have been proposed in the past for delineation of WMLs; some of these automated methods were based on single modality MRI [11; 12], whereas others used multimodal MRI [13; 14]. Likewise, a subset of these methods has employed voxel-based classification approach [15; 16], whereas others have utilized intensity distribution of the FLAIR signal to determine an optimal threshold to segment WMLs [17]. Some recent methods have utilized deep learning approaches, e.g., [PERSON] et al. used multi-scale convolutional neural network (CNN) on T1, FLAIR, and T\({}_{1}\)-weighted inversion recovery image as input to segment WMLs and normal brain structures [18], and [PERSON] et al. utilized CNN with global spatial information in MRI with none or mild vascular pathology for delineation of WMLs [19]. An important limitation of existing literature is that ensemble approaches have not been leveraged extensively and segmentation has been carried out either using conventional learning or deep learning approaches. Also, to the best of our knowledge, an analysis of WMLs of different brain regions to provide an insight into the differential characteristics of WMLs growing in different brain regions has not been conducted before. In this paper, we propose a novel ensemble classification-based segmentation approach [20] for automated segmentation of WMLs (Figure 1). The ensemble combines the strengths of deep learning and classical machine learning by employing base classifiers from each of the categories. Support vector machines (SVM) and residual neural network (ResNet) are used from conventional machine learning and deep learning domains, respectively. Handcrafted and transfer learning-based features are used to train SVM (linear, radial basis function, and sigmoid) models (six models), whereas patches extracted in a certain neighborhood of each voxel are used to train ResNet (five models). We also present a simple post-processing method based on connected-component generation to eliminate false positives.