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      A Multiple Sclerosis Recognition via Hu Moment Invariant and Artificial Neural Network Trained by Particle Swarm Optimization

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          Abstract

          Multiple sclerosis can damage the central nervous system, and current drugs are difficult to completely cure symptoms. The aim of this paper was to use deep learning methods to increase the detection rate of multiple sclerosis, thereby increasing the patient’s chance of treatment. We presented a new method based on hu moment invariant and artificial neural network trained by particle swarm optimization. Our method was carried out over ten runs of ten-fold cross validation. The experimental results show that the optimization ability of particle swarm optimization algorithm is superior to the genetic algorithm, simulated annealing algorithm and immune genetic algorithm. At the same time, compared with the HWT+PCA+LR method and the WE-FNN-AGA method, our method performs better in the performance of the detection.

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          Most cited references 32

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          Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis

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            Treatment of multiple sclerosis — success from bench to bedside

            The modern era of multiple sclerosis (MS) treatment began 25 years ago, with the approval of IFNβ and glatiramer acetate for the treatment of relapsing-remitting MS. Ten years later, the first monoclonal antibody, natalizumab, was approved, followed by a third important landmark with the introduction of oral medications, initially fingolimod and then teriflunomide, dimethyl fumarate and cladribine. Concomitantly, new monoclonal antibodies (alemtuzumab and ocrelizumab) have been developed and approved. The modern era of MS therapy reached primary progressive MS in 2018, with the approval of ocrelizumab. We have also learned the importance of starting treatment early and the importance of clinical and MRI monitoring to assess treatment response and safety. Treatment decisions should account for disease phenotype, prognostic factors, comorbidities, the desire for pregnancy and the patient's preferences in terms of acceptable risk. The development of treatment for MS during the past 25 years is a fantastic success of translational medicine.
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              Visual pattern recognition by moment invariants

               Ming-Kuei Hu (1962)
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                Author and article information

                Contributors
                yudongzhang@ieee.org
                shuihuawang@ieee.org
                liushuai@hunnu.edu.cn
                HanJi@home.hpu.edu.cn
                housm@163.com
                Journal
                978-3-030-51103-6
                10.1007/978-3-030-51103-6
                Multimedia Technology and Enhanced Learning
                Multimedia Technology and Enhanced Learning
                Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II
                978-3-030-51102-9
                978-3-030-51103-6
                13 June 2020
                2020
                : 327
                : 254-264
                Affiliations
                [15 ]GRID grid.9918.9, ISNI 0000 0004 1936 8411, School of Informatics, , University of Leicester, ; Leicestershire, UK
                [16 ]GRID grid.9918.9, ISNI 0000 0004 1936 8411, University of Leicester, ; Leicestershire, UK
                [17 ]Human Normal University, Changsha, China
                [18 ]GRID grid.412097.9, ISNI 0000 0000 8645 6375, Henan Polytechnic University, ; Jiaozuo, 454000 Henan China
                [19 ]Hebi Automotive Engineering Professional College, Hebi, 454030 Henan China
                Article
                22
                10.1007/978-3-030-51103-6_22
                7991434
                © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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                © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

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