Open AccessEARLIER DETECTION OF ALZHEIMER’S DISEASE USING IMAGE PROCESSING AND MACHINE LEARNING ALGORITHMS WITH GRAPH THEORY
Author(s) -
Devishree Naidu,
G. Anand Kumar Reddy
Publication year - 2021
Publication title -
international journal of computer science and mobile computing
Language(s) - English
Resource type - Journals
ISSN - 2320-088X
DOI - 10.47760/ijcsmc.2021.v10i08.006
Subject(s) - disease , graph , cognition , computer science , functional magnetic resonance imaging , alzheimer's disease , brain disease , machine learning , cognitive impairment , artificial intelligence , graph theory , psychology , cognitive psychology , neuroscience , medicine , theoretical computer science , pathology , mathematics , combinatorics
Alzheimer’s disease is one of the brain disease which is irreversible, progressive brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. There is no cure for Alzheimer’s disease but we prevent it’s by early detection. In existing work, limited with Alzheimer’s are irreversible, effect on daily activities, high memory loss and reducing the size of brain, etc. previous works focused on 2D and 3D formats now we considering 4D images. In proposed work, this work aims to present an automated method that assists in the diagnosis of Alzheimer’s disease supports the monitoring of the progression of the disease. The study of brain network based on resting-state functional Magnetic Resonance Imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize various aspects of the brain network by calculating measures the accuracy of different machine learning methods and different features to classify Cognitively Normal (C.N) individuals from Alzheimer’s Disease (A.D) and to predict longitudinal outcomes in participants with Mild Cognitive Impairment (MCI).