Modified Grey Wolf Randomized Optimization in Dementia Classification Using MRI Images

ABSTRACT

In this research work, a novel Modified Grey Wolf Randomized Optimization technique is used to classify the brain MRI images into two classes i.e. Non-Dementia or Dementia class. The swarm intelligence algorithms like Grey Wolf Optimization technique is predominantly used in solving optimization and feature selection problems. But using it to categorize brain MRI images for dementia classification will be incisive. Cross-sectional MRI of 65 Non-Dementia and 52 Dementia subjects collected from OASIS are used in the work. The original grey wolf optimization offers very poor performance due to its randomness and so randomness is removed and control parameters are inserted in modified grey wolf optimization. But the accuracy of this technique is not satisfactory due to the local optima problem. Hence the novel modified grey wolf randomized optimization with controlled randomness at an appropriate position of the original algorithm. Further Principal Component Analysis, Detrended fluctuation analysis, Hilbert Transform, K-Means clustering are added to the modified Grey Wolf Optimization versions for accuracy improvement and evaluated with and without statistical features. The highest accuracy of 93.16% is provided by Hilbert Transform-Modified Grey Wolf Randomized Optimization technique without statistical features while 51.28% and 52.99% accuracy is achieved by the original Grey Wolf Optimization with and without statistical features respectively.

KEYWORDS:

• Dementia
• Grey wolf optimization
• HS
• meta-heuristic
• MGWO
• MGWRO
• MRI
• PSO
• Swarm Intelligence

Additional information

Notes on contributors

N. Bharanidharan

N Bharanidharan received his BE degree in electronics and communication engineering from Algappa Chettiar College of Engineering, Karaikudi, India, in 2013; ME degree in communication systems from Thiagarajar College of Engineering, India, in 2015, and is currently pursuing PhD degree in information and communication engineering from Anna University, India. Presently, he is working as assistant professor in the Department of Electronics and Communication Engineering at Bannari Amman Institute of Technology, India. His research interests include machine learning algorithms and medical image processing.

R. Harikumar

R Harikumar received his BE degree in electronics and communication engineering from Regional Engineering College Trichy, India, in 1988; ME degree in applied electronics from College of Engineering, Gunidy, Anna University Chennai, India, in 1990, and PhD degree in information and communication engineering from Anna University, India. Presently, he is working as professor in the Department of Electronics and Communication Engineering at Bannari Amman Institute of Technology, India. His research interests include bio medical signals processing, soft computing. Email: [email protected]