Data mining approach based on harris hawks optimization (HHO) algorithm for multiple sclerosis lesions segmentation on brain magnetic resonance images
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Segmentation approaches, the majority of which are based on the combination of data mining
and machine learning techniques, can be used to extract damaged sections of brain tissues.
Clustering algorithms, such as the fuzzy C-means (FCM) cluster algorithm, are effective for
segmentation since they are accurate yet not unduly sensitive to visually noise. As a
consequence, while the optimum cluster center selection might impact segmentation, the FCM
approach is acceptable for detecting Multiple Sclerosis. It's complicated to choose them since
it's an NP-hard problem. Harris Hawks optimization (HHO) was utilized to determine the
optimal cluster center for both segmentation and FCM methods. Other common methods that
aren't as exact as HHO include the genetic algorithm and particle swarm optimization. In the
suggested procedure, each membership matrix must either be a hawk or an HHO. To reduce
Multiple Sclerosis clustering mistakes, the next step is to construct hawk populations or
membership vectors, with the best one chosen to locate the optimal cluster centers. The
suggested technique outperformed FCM clustering, as well as other methods like the support vector machine, k-NN algorithm, and hybrid data mining methods, in accuracy testing on a
variety of brain MRIs.
Açıklama
Anahtar Kelimeler
Data mining, Fuzzy C-Means (FCM) Clustering, Segmentation, Harris Hawks Optimization (HHO), MS
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Iswisi, Amal F. A. (2022). Data mining approach based on harris hawks optimization (HHO) algorithm for multiple sclerosis lesions segmentation on brain magnetic resonance images. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
