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Detection of Alzheimer’s disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images

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Abstract

The automated magnetic resonance imaging (MRI) processing techniques are gaining more importance in Alzheimer disease (AD) recognition, because it effectively diagnosis the pathology of the brain. Currently, computer aided diagnosis based on image analysis is an emerging tool to support AD diagnosis. In this research study, a new system is developed for enhancing the performance of AD recognition. Initially, the brain images were acquired from three online datasets and one real-time dataset such as AD Neuroimaging Initiative (ADNI), Minimal Interval Resonance Imaging in AD (MIRIAD), and Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS). Then, adaptive histogram equalization (AHE) and grey wolf optimization based clustering algorithm (GWOCA) were applied for denoising and segmenting the brain tissues; grey matter (GM), cerebro-spinal fluid (CSF), and white matter (WM) from the acquired images. After segmentation, the feature extraction was performed by utilizing dual tree complex wavelet transform (DTCWT), local ternary pattern (LTP) and Tamura features to extract the feature vectors from the segmented brain tissues. Then, ReliefF methodology was used to select the active features from the extracted feature vectors. Finally, the selected active feature values were classified into three classes [AD, normal and mild cognitive impairment (MCI)] utilizing deep neural network (DNN) classifier. From the simulation result, it is clear that the proposed framework achieved good performance in disease classification and almost showed 2.2–6% enhancement in accuracy of all four datasets.

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Abbreviations

AHE:

Adaptive histogram equalization

AD:

Alzheimer disease

ADNI:

Alzheimer Disease Neuroimaging Initiative

CSF:

Cerebro-spinal fluid

CNN:

Convolutional neural network

DM2L:

Deep multitask multichannel learning

DNN:

Deep neural network

GLCM:

Gray level co-occurrence matrix

GM:

Grey matter

GWOCA:

Grey wolf optimization based clustering algorithm

HMM:

Hidden Markov models

LBP:

Local binary pattern

LTP:

Local ternary pattern

LSTM:

Long short-term memory

LR:

Lucy Richardson

MRI:

Magnetic resonance imaging

MCI:

Mild cognitive impairment

MIRIAD:

Minimal Interval Resonance Imaging in AD

NIMHANS:

National Institute of Mental Health and Neuro Sciences

NN:

Neural network

OASIS:

Open Access Series of Imaging Studies

PDF:

Probability distribution function

DTCWT:

Tree complex wavelet transform

WM:

White matter

\(A\) :

Partitioned instances

\(A\,\mathrm{a}\mathrm{n}\mathrm{d}\,C\) :

Coefficient vectors

\(c\) :

Centre pixel

\(C\) :

Class of cluster

\({C}^{*}\) :

Optimal clusters

\(D\) :

Distance

\(f(X,C)\) :

Statistical function

\({g(Z}_{j})\) :

Pooling

\({g}_{0}\left(n\right)\) and \({g}_{1}\left(n\right)\) :

Low and high pass filter for second wavelet tree

\({h}_{0}\left(n\right)\) and \({h}_{1}\left(n\right)\) :

Low and high pass filter for first wavelet tree

\({H}_{j}\) :

Nearest hit instances

\(k\) :

Cluster center

\(m\) :

Hidden nodes

\({M}_{j}\) :

Nearest miss instances

\(N\) :

Number of population

\({O}_{f}\) :

Optimized value

\(p\) :

Neighbouring pixel

\(\widehat{p}j\) :

Sparsity penalty

\({\overrightarrow{\mathrm{r}}}_{1}\) and \({\overrightarrow{\mathrm{r}}}_{2}\) :

Random values

\({r}_{i}\) :

Randomly picked the instances

\(t\) :

Current iteration

\({T}_{c}\) :

Threshold constant

\({w}_{ij}\) :

Model parameter

\(W\left[A\right]\) :

Quality estimation

\({x}_{1},{x}_{2},\ldots,{x}_{n}\) :

Input data instances

\({\widehat{x}}_{i}\) :

Auto encoder hidden unit

\({X}_{prey}\) :

Position of prey

\({X}_{wolf}\) :

Grey wolf position

\(\alpha\) :

Alpha solution

\(\beta\) :

Beta solution

\({\Delta}H\) :

Horizontal convolved grey-scale images

\({\Delta}V\) :

Vertical convolved grey-scale images

\(\delta\) :

Delta solution

\(\theta\) :

Directionality

\(\lambda\) :

Weight delay

\({\mu }_{4}\) :

Fourth moment of mean

\(\sigma\) :

Variance

\({\Psi }_{h}\left(t\right)\) :

First wavelet tree

\({\Psi }_{g}\left(t\right)\) :

Second wavelet tree

\(\Omega\) :

Omega solution

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Authors and Affiliations

  1. Department of ECE, PET Research Centre, Mandya, University of Mysore, Mysore, India

    Halebeedu Subbaraya Suresha

  2. Department of ECE, P.E.S College of Engineering, Mandya, India

    Srirangapatna Sampathkumaran Parthasarathy

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  1. Halebeedu Subbaraya Suresha
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  2. Srirangapatna Sampathkumaran Parthasarathy
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Correspondence to Halebeedu Subbaraya Suresha.

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Suresha, H.S., Parthasarathy, S.S. Detection of Alzheimer’s disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images. Distrib Parallel Databases 40, 627–655 (2022). https://doi.org/10.1007/s10619-021-07345-y

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