Open AccessDesign of A Convolutional Neural Network System to Increase Diagnostic Efficiency of Alzheimer’s Disease
Author(s) -
Faransi Al-azdi,
Rossi Passarella,
Ari Susanto,
Cynthia Caroline,
RA Dwi Puspa,
Timotius Wira Yudha
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/648/1/012018
Subject(s) - convolutional neural network , grey matter , white matter , artificial intelligence , computer science , pattern recognition (psychology) , feature (linguistics) , feature extraction , artificial neural network , alzheimer's disease , neuroimaging , masking (illustration) , disease , magnetic resonance imaging , neuroscience , medicine , pathology , radiology , psychology , art , linguistics , philosophy , visual arts
The most common degenerative neural disease, Alzheimer’s disease (AD), is insidious and almost always requires imaging modalities to be diagnosed early. MRI is the most common one used, but requires timely interpretation. Here we develop a convolutional neural network (CNN)-based system that determines whether a brain MR image has AD or normal. First, feature extraction is performed to separate various parts of the brain. Then, the data is processed to differentiate normal brain from AD brain, solely using MR image. Finally, the neural network is supplemented using data from the patient’s history and physical examination. In this first phase, we were able to extract features from the brain MR image, initially by masking the image and separating the white matter, grey matter, and cerebrospinal fluid called the grey level co-occurrence method (GLCM). This method is able to using a convolutional neural network.