applied sciences  
An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLQ  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4,*\({}^{\copyright}\), [PERSON] 5,6,**[PERSON] 7,**[PERSON] 7,**[PERSON] 8,9 and [PERSON] 10,11,12  
1 Department of Radiology, Beyhekim Training and Research Hospital, Konya 42001, Turkey;  
[EMAIL_ADDRESS]  
2 Vocational School of Technical Sciences, Firat University, Elazig 23119, Turkey; [EMAIL_ADDRESS]  
3 Department of Neurology, School of Medicine, Malatya Turgut Ozal University, Malatya 44100, Turkey;  
[EMAIL_ADDRESS]  
4 Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1 WB, UK  
5 School of Business (Information System), University of Southern Queensland,  
Towoomba, QLD 4350, Australia; [EMAIL_ADDRESS]  
6 Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia  
7 Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey;  
[EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (T.T.) 8 Department of Cardiology, National Heart Centre Singapore, Singapore 599489, Singapore;  
[EMAIL_ADDRESS]  
9 Duke-NUS Medical School, Singapore 169857, Singapore  
10 Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore;  
[EMAIL_ADDRESS]  
11 Department of Biomedical Engineering, School of Science and Technology, SUSS University,  
Singapore 599491, Singapore  
12 Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 413, Taiwan  
* Correspondence: [EMAIL_ADDRESS]  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLQ. _Appl. Sci._**2022**, 12, 4920. [[https://doi.org/10.3390/app12104920](https://doi.org/10.3390/app12104920)]([https://doi.org/10.3390/app12104920](https://doi.org/10.3390/app12104920))  
Academic Editor: [PERSON]  
Received: 7 April 2022  
Accepted: 10 May 2022  
Published: 12 May 2022  
**Publisher:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **Abstract:** Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (\(n=1652\)), sagittal images only (\(n=1775\)), and combined axial and sagittal images (\(n=3427\)) of both MS and healthy classes. All images were resized to 224 \(\times\) 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA).