{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:46:27Z","timestamp":1787165187849,"version":"build-2736575974"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that prominently affects young adults due to its debilitating nature. The pathogenesis of the disease is focused on the inflammation and neurodegeneration processes. Inflammation is associated with relapses, while neurodegeneration emerges in the progressive stages of the disease. Magnetic resonance imaging (MRI) is commonly used for the diagnosis of MS, and guidelines such as the McDonald criteria are available. MRI is an essential tool to demonstrate the spatial distribution and changes over time in the disease. This study discusses the use of image processing techniques for the diagnosis of MS and specifically combines the MobileNetV2 network with exemplar-based learning, IMrMr feature selection, and K-Nearest Neighbors (KNN) classification methods. Experiments conducted on two different datasets (Dataset 1 and Dataset 2) demonstrate that these methods provide high accuracy in diagnosing MS. Dataset 1 comprises 128 patients with 706 MRI images, 131 MS patients with 667 MRI images, and 150 healthy control subjects with 1373 MRI images. Dataset 2 includes an MS group with 650 MRI images and a healthy control group with 676 MRI images. The results of the study include 10-fold cross-validation results performed on different image sections (Axial, Sagittal, and Hybrid) for Dataset 1. Accuracy rates of 99.76% for Axial, 99.48% for Sagittal, and 98.02% for Hybrid sections were achieved. Furthermore, 100% accuracy was achieved on Dataset 2. In conclusion, this study demonstrates the effective use of powerful image processing methods such as the MobileNetV2 network and exemplar-based learning for the diagnosis of MS. These findings suggest that these methods can be further developed in future research and offer significant potential for clinical applications in the diagnosis and monitoring of MS.<\/jats:p>","DOI":"10.3390\/diagnostics13193030","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T10:46:21Z","timestamp":1695552381000},"page":"3030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis"],"prefix":"10.3390","volume":"13","author":[{"given":"Tuba","family":"Ekmekyapar","sequence":"first","affiliation":[{"name":"Department of Neurology, Elaz\u0131\u011f Fethi Sekin City Hospital, Elazig 23280, Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-0946","authenticated-orcid":false,"given":"Burak","family":"Ta\u015fc\u0131","sequence":"additional","affiliation":[{"name":"Vocational School of Technical Sciences, Firat University, Elazig 23119, Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1097\/WCO.0000000000000622","article-title":"Multiple sclerosis: Clinical aspects","volume":"31","author":"Oh","year":"2018","journal-title":"Curr. Opin. Neurol."},{"key":"ref_2","first-page":"1502","article-title":"Coles","volume":"372","author":"Compston","year":"2008","journal-title":"Multiple sclerosis. Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1177\/1352458517694432","article-title":"New insights into the burden and costs of multiple sclerosis in Europe","volume":"23","author":"Kobelt","year":"2017","journal-title":"Mult. Scler."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1093\/brain\/awp070","article-title":"The relation between inflammation and neurodegeneration in multiple sclerosis brains","volume":"132","author":"Frischer","year":"2009","journal-title":"Brain"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/ene.13819","article-title":"Multiple sclerosis\u2014A review","volume":"26","author":"Dobson","year":"2019","journal-title":"Eur. J. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1093\/brain\/awq076","article-title":"Evidence for a two-stage disability progression in multiple sclerosis","volume":"133","author":"Leray","year":"2010","journal-title":"Brain"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s00415-005-0934-5","article-title":"The window of therapeutic opportunity in multiple sclerosis: Evidence from monoclonal antibody therapy","volume":"253","author":"Coles","year":"2006","journal-title":"J. Neurol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jns.2013.05.010","article-title":"Pathology and disease mechanisms in different stages of multiple sclerosis","volume":"333","author":"Lassmann","year":"2013","journal-title":"J. Neurol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1056\/NEJM199801293380502","article-title":"Axonal transection in the lesions of multiple sclerosis","volume":"338","author":"Trapp","year":"1998","journal-title":"The N.Engl. J. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/S1474-4422(15)00393-2","article-title":"MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines","volume":"15","author":"Filippi","year":"2016","journal-title":"Lancet Neurol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/S1474-4422(17)30470-2","article-title":"Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria","volume":"17","author":"Thompson","year":"2018","journal-title":"Lancet Neurol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.inffus.2023.03.022","article-title":"Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals","volume":"96","author":"Tasci","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_13","unstructured":"Ta\u015fci, B. (2023). Deep Learning Applications in Image Analysis, Springer."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tasci, B. (2023). Automated ischemic acute infarction detection using pre-trained CNN models\u2019 deep features. Biomed. Signal Process. Control., 82.","DOI":"10.1016\/j.bspc.2023.104603"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tas, N.P., Kaya, O., Macin, G., Tasci, B., Dogan, S., and Tuncer, T. (2023). ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI. Biomedicines, 11.","DOI":"10.3390\/biomedicines11092441"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tasci, B., and Tasci, I. (2022). Deep feature extraction based brain image classification model using preprocessed images: PDRNet. Biomed. Signal Process. Control., 78.","DOI":"10.1016\/j.bspc.2022.103948"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bejarano, B., Bianco, M., Gonzalez-Moron, D., Sepulcre, J., Go\u00f1i, J., Arcocha, J., Soto, O., Carro, U.D., Comi, G., and Leocani, L. (2011). Computational classifiers for predicting the short-term course of Multiple sclerosis. BMC Neurol., 11.","DOI":"10.1186\/1471-2377-11-67"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Siar, H., and Teshnehlab, M. (, January 29\u201331). Diagnosing and classification tumors and MS simultaneous of magnetic resonance images using convolution neural network. Proceedings of the 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Bojnord, Iran.","DOI":"10.1109\/CFIS.2019.8692148"},{"key":"ref_19","unstructured":"Eitel, F., Albrecht, J.P., Paul, F., and Ritter, K. (2019). Harnessing spatial MRI normalization: Patch individual filter layers for CNNs. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1002\/ima.22492","article-title":"Multiple sclerosis identification in brain MRI images using wavelet convolutional neural networks","volume":"31","author":"Alijamaat","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sepahvand, N.M., Hassner, T., Arnold, D.L., and Arbel, T. (2018, January 16\u201320). Cnn Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain. Revised Selected Papers; Part I.","DOI":"10.1007\/978-3-030-11723-8_6"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1148\/radiol.2019191061","article-title":"Deep learning for predicting enhancing lesions in multiple sclerosis from noncontrast MRI","volume":"294","author":"Narayana","year":"2020","journal-title":"Radiology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4776770","DOI":"10.1155\/2023\/4776770","article-title":"Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection","volume":"2023","author":"Krishnamoorthy","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1166\/jmihi.2019.2692","article-title":"High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model","volume":"9","author":"Zhang","year":"2019","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e210292","DOI":"10.1148\/ryai.210292","article-title":"Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning","volume":"4","author":"Zhuo","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119787","DOI":"10.1016\/j.neuroimage.2022.119787","article-title":"Single-timepoint low-dimensional characterization and classification of acute versus chronic multiple sclerosis lesions using machine learning","volume":"265","author":"Caba","year":"2023","journal-title":"NeuroImage"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Y\u0131lmaz Acar, Z., Ba\u015f\u00e7ift\u00e7i, F., and Ekmekci, A.H. (2022). Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform. Biomed. Signal Process. Control, 78.","DOI":"10.1016\/j.bspc.2022.103940"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Macin, G., Tasci, B., Tasci, I., Faust, O., Barua, P.D., Dogan, S., Tuncer, T., Tan, R.-S., and Acharya, U.R. (2022). An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ. Appl. Sci., 12.","DOI":"10.3390\/app12104920"},{"key":"ref_31","unstructured":"Loizou, C. (2012). eHealth Lab-MRI Lesion Segmentation in Multiple Sclerosis Database, e. LU o. Cyprus, Ed."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3899","DOI":"10.1007\/s11042-021-11726-x","article-title":"A deep feature warehouse and iterative MRMR based handwritten signature verification method","volume":"81","author":"Tuncer","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"121314","DOI":"10.1016\/j.eswa.2023.121314","article-title":"Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system","volume":"236","author":"Tatli","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e101","DOI":"10.37175\/stemedicine.v2i8.101","article-title":"Multiple sclerosis detection via 6-layer stochastic pooling convolutional neural network and multiple-way data augmentation","volume":"2","author":"Wang","year":"2021","journal-title":"STEMedicine"},{"key":"ref_35","first-page":"161","article-title":"Diagnosis of multiple sclerosis disease in brain MRI images using convolutional neural networks based on wavelet pooling","volume":"9","author":"Alijamaat","year":"2021","journal-title":"J. AI Data Min."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/19\/3030\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:56:41Z","timestamp":1760129801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/19\/3030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,23]]},"references-count":35,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["diagnostics13193030"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13193030","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,23]]}}}