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

Abstract

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.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

“…Iswisi et al [ 47 ] developed a ML model for MS diagnosis based on the Harris Hawks optimization (HHO) algorithm using MRI scans of 10 patients. The fuzzy C-means (FCM) algorithm was combined with the HHO algorithm for the extraction of lesions and reduction of the segmentation error.…”

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

“…e early stages of the disease have no symptoms so that it can be easily diagnosed. Due to the annoyance of this disease and the similarity of cardiac arrhythmia symptoms to other heart diseases, designing an intelligent system to diagnose this disease seems necessary [2][3][4][5][6][7][8][9][10][11].…”

Improved multiple sclerosis diagnosis with advanced deep learning techniques

“…A biogeography‐based optimization method was proposed to address the FCM clustering algorithm issue and determine the initial cluster centers. This method combines the artificial bee colony (ABC) and PSO algorithm with the FCM segmentation technique, providing a solution for the initial clustering problem and outperforming the standard FCM clustering algorithm in terms of performance 21–24 . A krill herd optimization (KHO) algorithm was proposed for image segmentation based on multilevel thresholding, maximizing Kapur's or Otsu's objective function to determine the cutoff value and significantly reducing processing time compared to other methods 25,26 .…”