A machine learning approach for multiple sclerosis diagnosis through Detecron Architecture
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17055-5.pdf
Reference37 articles.
1. Kuhlmann T, Moccia M et al (2023) Multiple sclerosis progression: time for a new mechanism-driven framework. Lancet Neurol 22(1):78–88. https://doi.org/10.1016/S1474-4422(22)00289-7
2. Martins T, Carvalho V, Soares F, Leão C (2023) Physioland: a motivational complement of physical therapy for patients with neurological diseases. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16051-z
3. Rodríguez S, Mauricio F et al (2022) The immune response in multiple sclerosis. Annu Rev Pathol 17:121–139. https://doi.org/10.1146/annurev-pathol-052920-040318
4. Xinda Z, Claire J (2023) Mechanisms of demyelination and remyelination strategies for multiple sclerosis. Int J Mol Sci 24(7):6373. https://doi.org/10.3390/ijms24076373
5. Boesen S, Blinkenberg M et al (2022) Magnetic resonance imaging criteria at onset to differentiate pediatric multiple sclerosis from acute disseminated encephalomyelitis: a nationwide cohort study. Multiple Scler Relat Disord 62:103738. https://doi.org/10.1016/j.msard.2022.103738
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