3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI

Bijen Khagi - Department of Information and Communication Engineering, Chosun University, Dong-Gu, Gwangju 501-759, Republic of Korea
Goo-Rak Kwon - Department of Information and Communication Engineering, Chosun University, Dong-Gu, Gwangju 501-759, Republic of Korea

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.5.2.572


A recent study from MRI has revealed that there is a minor increase in cerebral-spinal fluid (CSF) content in brain ventricles and sulci, along with a substantial decrease in grey matter (GM) content and brain volume among Alzheimer's disease (AD) patients. It has been discovered that the grey matter volume shrinkage may indicate the possible case of dementia and related diseases like AD. Clinicians and radiologists use imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scan, and Positron Emission Tomography (PET) to diagnose and visualize the tissue contents of the brain. Using the whole brain MRI as the feature is an on-going approach among machine learning researchers, however, we are interested only in grey matter content. First, we segment the MRI using the SPM (Statistical parameter mapping) tool and then apply the smoothing technique to get a 3D image of grey matter (later called as grey version) from each MRI. This image file is then fed into 3D convolutional neural network (CNN) with necessary pre-processing so that it can train the network, to produce a classifying model. Once trained, an untested MRI (i.e. its grey version) can be passed through the CNN to determine whether it is a healthy control (HC), or Mild Cognitive Impairment (MCI) due to AD (mAD) or AD dementia (ADD). Our validation and testing accuracy are reported here and compared with normal MRI and its grey version.


Alzheimer disease (AD); Magnetic resonance imaging (MRI); Statistical parameter mapping (SPM); Convolutional neural network (CNN).

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