Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

• Published in: BioMed research international Vol. 2021; no. 1; p. 3248834
• Main Authors: Iswisi, Amal F. A., Karan, Oğuz, Rahebi, Javad
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Animals; Behavior; Brain; Brain - pathology; Brain damage; Brain diseases; Care and treatment; Clustering; Cooperation; Data mining; Diagnosis; Fuzzy Logic; Genetic algorithms; Hawks - physiology; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Image segmentation; Knowledge discovery; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical optimization; Medical diagnosis; Medical imaging; Methods; Multiple sclerosis; Multiple Sclerosis - diagnosis; Multiple Sclerosis - pathology; Neuroimaging; Neuroimaging - methods; Noise sensitivity; Optimization algorithms; Particle swarm optimization; Pattern Recognition, Automated - methods; Population; Risk factors; Support Vector Machine; Support vector machines; China
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2021/3248834

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.