## 1 Introduction
These limitations provide an opportunity for harnessing the power of artificial intelligence (AI) for screening large numbers of MRI images to detect MS, which can be posed as a problem of classifying images with and without white matter lesions [17]. AI mimics human intelligence to perform tasks and can progressively attain higher accuracy by collecting more information [18]. These desirable traits have spurred the adoption of AI methods in many healthcare applications, which can potentially ease the workload ofmedical and paramedical personnel. To this end, AI has been used for computer-aided MS diagnosis [19; 20] and prognostication of disease progression [19; 20]. Accurate AI-enabled MS detection promises earlier diagnosis and treatment initiation with DMTs, better disease surveillance, and more efficient utilization of healthcare resources. However, questions remain about the reliability and practicality of AI-enabled MS detection. Some MS detection studies presented in the literature are given below. [PERSON] et al. [21] analyzed the MRIs of 373 MS patients using a CNN model and attained accuracy rates of 83.3%, 67.7%, and 85.7% for clinical, cognitive, and combined clinical plus cognitive diagnoses, respectively. The parameter values and optimization methods used in the CNN architecture negatively affected the classification results. [PERSON] et al. [22] proposed a method that incorporated a two-dimensional discrete Haar wavelet transform and CNN to study the MRIs of 38 patients and 20 healthy individuals and attained sensitivity, specificity, precision, and accuracy of 99.14%, 98.89%, 99.43%, and 99.05%, respectively, in their experiments. [PERSON] et al. [23] proposed a method for measuring plaque volume using MRIs from four different datasets. Their proposed method achieved 99.69%, 98.51%, 98.51%, and 99.85% accuracy, precision, sensitivity, and specificity. [PERSON] et al. [24] studied Tl, T2, and FLAIR MRIs of 489 healthy and 519 MS patients. Using the Vgg16+FCN network structure, they attained 72%, 70%, and 70% sensitivity, specificity, and accuracy. [PERSON] et al. [25] studied MRIs of 124 MS patients. Using a RimNet CNN architecture, they attained 62.3%, 75.8%, 95.1%, and 93.8% F1-score, sensitivity, specificity, and accuracy. [PERSON] et al. [26] used diffusion-based spectrum imaging techniques to study the MRIs of 38 MS patients. Using a deep neural network, they attained 97.3%, 99.1%, 97.3%, and 93.4% F1-score, sensitivity, specificity, and accuracy. [PERSON] and [PERSON] [27] studied a large dataset of 1855 healthy MRIs, 2910 MRIs from 616 MS patients, and 639 MRIs from 625 leukoencephalopathy patients attained precision and recall rates of 92% and 89%, respectively. [PERSON] and [PERSON] [28] studied the MRIs of 38 MS patients using a 2D-CNN network and attained 99.55%, 99.15%, and 99.15% accuracy, precision, and recall, respectively. [PERSON] et al. [29] used 127 scans from the Medical Image Computing and Computer-Assisted Intervention 2016 and International Symposium on Biomedical Imaging 2015 datasets in their study. In their segmentation study, they attained 67%, 48%, and 90% dice similarity coefficient, sensitivity, and precision. [PERSON] et al. [30] conducted two experiments using MRIs of 21 MS patients using a CNN network model and attained 83.3% and 100% accuracy rates in the first and second experiments, respectively. The datasets in the studies presented above [22; 28; 29; 30] are small. In [PERSON] et al. [21] and [PERSON] et al. [24], the dataset is large, but has a low accuracy rate. In [21; 22; 23; 24; 25; 26; 27; 28; 30], computational complexity is high. With our work on an accurate MS detection model, we attempt to address some of the issues and problems raised above. Our solution took the form of a reliable machine learning model for classifying FLAIR images of the brain into MS and non-MS classes accurately. To this end, we created a novel exemplar feature engineering algorithm based on local phase quantization (LPQ), which we named exemplar multiple parameters LPQ (ExMPLPQ).