Automated Diagnosis of Early Alzheimer’s disease using Fuzzy Neural Network

TLDR: The aim of this research is to develop computing algorithms that can partially or fully automate the extraction of features from MRI of neuroanatomical structures in MTL regions, which aid in diagnosis of AD.

Abstract: This paper is presented towards the development of an automated diagnosis of Alzheimer’s disease (AD) from Magnetic Resonance Images (MRI) using Fuzzy Neural Network (FNN) algorithm. AD is a chronic degenerative disease of the central nervous system. The diagnosis of AD at an early stage is a major concern due to the growing number of the elderly population affected, as well as the lack of a standard and effective diagnosis procedure available to the healthcare providers. Medial Temporal Lobe (MTL) structure in brain has been reported to be involved earliest and most extensively in the pathology of AD. The aim of this research is to develop computing algorithms that can partially or fully automate the extraction of features from MRI of neuroanatomical structures in MTL regions, which aid in diagnosis of AD. Hippocampus volume reductions and ventricular expansions are observed and play significant role in MTL region of brain to identify AD, various other features are also considered and measured. The extracted feature values may be uncertain and it introduces fuzziness in input given to the Artificial Neural Network (ANN). Input uncertainty distribution is effectively solved by designing FNN. The back-propagation neural network algorithm was applied to the analysis of regional patterns corresponding to AD. A trained network was able to successfully classify MRI scans of normal subjects from Mild Cognitive Impairment (MCI), which could be a valuable early indicator of AD. This automated diagnosis will help the neurologist to find the level of disorders and measure the development stage of atrophy in the brain.