D. Karthikeyan

Bio: D. Karthikeyan is an academic researcher from VIT University. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

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

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.

Imaging and machine learning techniques for diagnosis of Alzheimer's disease.

Abstract: Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.