Performance evaluation of bacterial foraging optimization algorithm for the early diagnosis and tracking of Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a complex progressive brain disorder. The concept of Mild Cognitive impairment (MCI) is considered as a subtle but measurable disorder that is greater than the normal aging controls. In this paper we explored the ability of specifically designed and trained Artificial Neural Network (ANN) combined with bacterial foraging optimization (BFO) algorithm, to discriminate between the MRI of patients with AD and their age matched controls. The proposed approach employs feature extraction using Gabor filter and discrimination based on BFO tuned ANN. Due to the progressive nature of AD, This study aims to identify the structural characteristics at baseline and over a period of two years that could serve as accurate predictors of future development of MCI in the NCI and AD in the MCI patients .We report the results of the classification accuracies on both training and test images are up to 92%. © 2012 Springer-Verlag.