Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm.
The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique,...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/27/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154337683&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154337683 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/27/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154337683 154337683 154337683 10.1155/2021/3248834 154337683 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm. aug: au: Iswisi, Amal F. A. Karan, Oğuz Rahebi, Javad affil: School of Engineering and Architecture, Electrical and Computer Engineering, Altınbaş University, Istanbul, Turkey sug: subj: Multiple Sclerosis Diagnosis Brain Radiography Magnetic Resonance Imaging Algorithms Human Membership Cluster Analysis Data Mining Support Vector Machine Logic ab: The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k -NN algorithm, support vector machine, and hybrid data mining methods in accuracy. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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