Open AccessMachine Learning Approach for Automatic Detection of Alzheimers Disease using Resting State fMRI
Author(s) -
K. Emily Esther Rani
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36170
Subject(s) - artificial intelligence , support vector machine , computer science , random forest , pattern recognition (psychology) , feature selection , thresholding , discrete wavelet transform , resting state fmri , machine learning , perceptron , neuroimaging , computer aided diagnosis , artificial neural network , wavelet transform , wavelet , image (mathematics) , medicine , psychiatry , psychology , neuroscience
Alzheimer’s Disease (AD) is a neurological disease that affects memory and the livelihood of the people that are diagnosed with it. Efficient automated techniques for early diagnosis of AD is very important because early diagnosis is used to prevent a patient from death. In this work, we present a novel computer-aided diagnosis (CAD) techniques using machine learning algorithms for the early diagnosis of AD. The input resting state fMRI(rsfMRI) images are taken from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The input image is pre-processed using Discrete Wavelet Transform(DWT). Automated thresholding algorithm is used to segment the image. Then, the segmented resting state fMRI images are used to extract useful and informative features. The best features are selected by Fisher’s code feature selection algorithm. Finally, an automated Image classification step is performed using machine learning algorithms Support Vector Machine(SVM), Decision Tree , Random Forest and Multi-Layer Perceptron algorithms to distinguish between normal patients and AD patients.