Krill Herd Optimized Feature Selection for Classification of Alzheimer’s Disease from MRI Images - Sumanth, S; A Suresh, Dr. (International Journal of Engineering and Technology, 2018)
Alzheimerâ€™s disease (AD) is a type of dementia that is difficult to detect based on clinical surveillances. AD detection on brain Magnetic Resonance Imaging (MRI) data is major anxiety in the neurosciences. Conventional evaluation of efficient image scans in general relies on manual reorientation, visual reading and semi quantitative exploration in brain sections. The Feature Selection (FS) has been tackled to a greater extent since it has proved itself to be a technique that is able to solve the computational problems that are NP-hard and for finding some optimal feature subsets. The FS works by means of removing the features which are irrelevant or redundant. Here in this work, a Krill Herd Optimized Feature Selection has been proposed for the classification of the MRI images. Using the Krill Herd Algorithm (KHA) happens to be widely accepted recently. This is owing to the fact that it represents a modern optimization that is effective and is a good search process. The segregation of the images from brain MRI into either normal or abnormal is important for analysing a normal patient and considering the ones that have higher chances of abnormalities. This technique of classification known as the fuzzy classifier along with the Neural Network has been proposed for getting a better performance that was accurate.Â Â