enhanced gaussian bat algorithm and doppler effect (egba

Enhanced Gaussian Bat Algorithm and

Doppler Effect (EGBA-DE) and Supervised

HFNN Approach for Effective Classification of

Alzheimer‟s Disease 1C. Geetha and

2D. Ramyachitra

1Department of Computer Science,

Bharathiar University.

Sri Kayaka Parameswari Arts & Science College for Women,

Chennai, India. 2Department of Computer Science,

Bharathiar University,

Coimbatore, India.

[email protected]

Abstract Dementia is a growing health problem and Alzheimer’s disease (AD) is

the leading cause of dementia in the elderly accounting for 50–60% of all

cases. Magnetic Resonance Imaging (MRI) has emerged as a significant tool

to recognize in-between biomarkers of AD appropriate to its capability to

calculate regional changes in the brain with the purpose of are thought to

reflect disease severity and progression. This paper develops a novel

methodology for classification of AD from MRI images designed for

medical support. In this paper, Enhanced Gaussian Bat Algorithm and

Doppler Effect (EGBA-DE) is proposed for preprocessing step for removing

the noise features. An interesting extension of the BA will be to use

different schemes of wavelength or frequency variations by using Gaussian

distribution function. Then the feature extraction is performed by using

Haar Wavelet Transform method. In this work, feature selection is

performed by Gaussian Membership Based Fuzzy C-Means (GMFCM). The

similarity between two features are computed using Euclidean distance

function, then irrelevant features in the feature set is removed from original

feature subset. Finally Supervised Hybrid Fuzzy Neural Network (SHFNN)

International Journal of Pure and Applied MathematicsVolume 119 No. 10 2018, 273-299ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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is used for classifying the features from the feature selection phase. The

features consequently derived are employed for the purpose of training a

SHFNN to classify features into three classes like Alzheimer’s, Mild

Alzheimer's and Huntington's disease. The experimental result shows that

the proposed SHFNN classifier provides better classification accuracy than

other classification approaches.

Index Terms:Dementia, Alzheimer’s disease (AD), Magnetic Resonance

Imaging (MRI), Feature Selection, Feature Extraction, Classification,

Enhanced Gaussian bat Algorithm and Doppler Effect (EGBA-DE,

Gaussian Membership based Fuzzy C-means (GMFCM), and Supervised

Hybrid Fuzzy Neural Network (SHFNN).

International Journal of Pure and Applied Mathematics Special Issue

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1. Introduction

Alzheimer‟s disease (AD) is an advancing neurodegenerative condition, which

leads to the loss of cognitive capabilities and memory, along with corresponding

high morbidity rate and expense to society [1]. Accurately diagnosing AD, in

addition to detecting the prodromal stage, Mild Cognitive Impairment (MCI)

comprise to be an significant primary step towards a remedy and have remained

in the focus of several neuroimaging studies. Magnetic resonance imaging

(MRI) is typically a neuroimaging technique, which can be utilized for

visualizing the brain anatomy with a greater degree of spatial resolution and

contrast between the types of brain tissue. Structural MRI techniques have been

utilized for identifying the regional volumetric variations in brain areas that are

observed to be related with AD and MCI, illustrating the use of such techniques

for examining this disease [1], [2]. Particularly, structural MRI has detected AD

and MCI-associated cross-sectional differences and longitudinal variations in

volume and size of certain brain areas, like the hippocampus and entorhinal

cortex, in addition to the regional variations in gray matter, white matter and

Cerebrospinal Fluid (CSF) on the basis of a voxel-after-voxel [2]. In the recent

times, MRI data have been highlighted in machine learning experiments that are

targeted at the classification of subjects, which are AD vs. Cognitively Normal

(CN) or MCI vs. CN [3]. The current techniques use network analysis [1], [4] or

employ machine learning directly over the voxels [2], [5]. However, these

techniques, just take a two-way classification paradigm, AD vs. CN, into

consideration, where an exact decision boundary between these groups can be

easily acquired. Practically, this advancing neurodegenerative disease is a

continuum, having the subjects moving through several stages ranging from

MCI to AD, rendering the classification to be much more tedious.

Currently, machine learning and pattern classification techniques have been

extensively used in designing a computer-aided brain disease diagnosis system

with the help of neuro images like Magnetic Resonance Imaging (MRI) ,

Positron Emission Tomography (PET) [6], functional MRI (fMRI) [7], and

Diffusion Tensor Imaging (DTI) [8]. Research works have indicated that

structural MRI is the most standardized form of imaging modality in clinical

practice [9] and it is also helpful in tracking various clinical stages of AD [10].

Hence, the technique is assessed on structural MR images. Multiple kind of

features can be acquired from the structural MRI of the entire brain, like

intensities or gray-matter densities [11], group comparison made of cortical

thickness [12], morphometry [13], and texture measures [14]. The integration of

various kinds of features can enhance the accuracy of the diagnosis made of AD

compared to techniques that utilize only one feature [15]. The important issue

that is encountered in AD classification includes preprocessing and feature

extraction that leads to inaccurate classification on MRI images. The already

available techniques render results that are inaccurate in segmentation and also

feature selection. Moreover, it faces challenges with computational complexity

owing to the number of iterations. This way, the overall performance of the


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MRI classification is decreased substantially. Therefore, the newly introduced

research work, MRI classification has gained focused in these new models.

Texture analysis could analyze the subtle changes of body; therefore, it has been

widely used in AD diagnosis for extracting texture features. Moreover, the

number of feature dimensions in neuroimaging is commonly higher than the

number of samples. In order to solve the problem of overfitting, it is necessary

to select features. In the classification based on neuroimaging, several feature

selection techniques have been proposed, for example, univariate methods,

multivariate approaches, perturbation method, and Support Vector Machine

Recursive Feature Elimination (SVM-RFE). Firstly, the optimization algorithm

is employed for preprocessing of noise removal. Then the feature extraction is

performed by using wavelet transformation methods for better classification

results. Thirdly improve the process of feature selection by introducing

clustering algorithm. Moreover, we also realized some proposed Supervised

Hybrid Fuzzy Neural Network (SHFNN). It shows good performance in

Alzheimer's disease diagnosis. Because of this, feature selection is taken as a

comparison method in this work.

2. Literature Review

Recently, dementia has emerged to be a critical threat to global health and

society. There was estimation that in the whole world, 35.6 million people were

affected by dementia in the year 2010.

Westman et al [16] utilized the hippocampal volume obtained on MRI data to

act as the features and the Orthogonal Partial Least Squares to Latent Structures

(OPLS) analysis to be the classifier to distinguish AD and MCI from elderly

normal individuals. When it is about the shape features, every segmented

hippocampus is defined by a series of Spherical Harmonics (SPHARM), whose

coefficients were calculated with the SPHARM-PDM software designed by the

University of North Carolina and the National Alliance for Medical Imaging

Computing.

George and Karnan [17] introduced a new technique for the enhancement of

MRI image that, in turn, is dependent on the Modified Tracking Algorithm,

Histogram Equalization and Center Weighted Median (CWM) filter. This

technique comprises of two schemes. The first scheme is the application of the

modified tracking algorithm to eliminate the film artifacts, labels and skull

region and therefore using the Histogram Equalization and CWM filter methods

individually for the images enhancement. Priyanka and Balwinder [18]

proposed a Median Filter approach for the removal of salt and pepper noise and

Poisson noise away from the images. The value of every pixel is fixed to

median value of the pixels close to the respective input pixels. Then this filter is

used for removing these noises and afterwards the bounding box approach is

used for detecting the tumor position.


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Ramalakshmi and Chandran [19] have developed an anisotropic filter for the

elimination of background noise and thereby protecting the edge points present

in the image. This technique is involved with concurrent filtering and contrast

stitching. The selection of a Diffusion constant is related to the noise gradient

and it smoothens by eliminating noises present in the background with the

filtering done with a right value of threshold. Gerardin et al [20] used two sets,

one for every hippocampus, of 3D SPHARM coefficients to be the features and

adopted a univariate feature selection technique integrated with a bagging

approach to extract the most distinguishing features. Atrophy in the earlier

stages of AD is not restricted to the hippocampus or the entorhinal cortex. Other

regions are also affected in AD patients and Mild Cognitive Impairment (MCI)

patients. Hence, multi-Region Of Interest (ROI)-based feature extraction has

gained a lot of research interest.

Liu and Shen [21] introduced a multi-channel pattern analysis technique to

evaluate the hypo-metabolism patterns of AD and MCI on Fluorodeoxyglucose

(FDG)-Positron Emission Tomography (PET) data and recognized 21 brain

areas to be the most distinguishing biomarkers. Sakthivel et al [22] focused on

including various types of information, along with textual data, image visual

features obtained from scans along with direct input from the doctor. Generally,

features can have coefficients corresponding to a spectral transform of image

signal, e.g. Fourier or Discrete Cosine Transform coefficients (DCT), statistics

on image gradients etc. Features utilized for describing brain images include

Local Binary Patterns (LBPs) and DCT.

Xiao et al [23] performed the extraction of combination of three diverse features

from structural MR images which are: gray-matter volume, Gray-Level Co-

Occurrence Matrix (GLCM), and Gabor feature. These features can get both the

2D and 3D information about brains, and the experimental results reveal that

achieving a better performance is possible through the multi feature fusion.

Khedher et al [24] introduced a new classification technique dependent on

Independent Component Analysis (ICA) and supervised learning technique is

introduced to be used on segmented brain MRI from Alzheimer‟s disease Neuro

Imaging Initiative (ADNI) subjects for the purpose of automatic classification

task. At last, every brain image from the database gets projected onto the space

that is spanned by this individual components basis for undergoing feature

extraction, and a Support Vector Machine (SVM) is adopted to deal with the

task of classification. An 87.5% accuracy in recognizing AD from NC, along

with 90.4% specificity and 84.6% sensitivity is received. Based on the

experimental results, this novel technique can successfully distinguish AD, MCI

and NC individuals.

With the features being estimated on training cases, a classifier could be trained

and used for predicting the diagnosis related to a testing case, whose features

are obtained in the same manner. Horn et al [25] adapted the image features that

are compressed by the Partial Least Squares (PLS) to LDA for discriminating


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AD from FTD and attained an accuracy of 84 %, a sensitivity of 83 %, and a

specificity of 86 % over perfusion Single-Photon Emission Computed

Tomography (SPECT) images. Lopez et al [26] used the multivariate

techniques, like PCA and LDA, for feature extraction, and thereafter used the

Bayesian framework for automated diagnosis of AD and NC employing PET

and SPECT. Sarraf et al [27] provided an overview of deep learning-based

pipelines used for distinguishing the Alzheimer‟s MRI and fMRI obtained from

normal healthy control data for a particular age group. It nearly exactly

discriminated the Alzheimer‟s patients from healthy normal brains. Experiments

carried out on the MRI dataset used with no skull-stripping preprocessing had

revealed that it performed better compared to various traditional classifiers in

terms of accuracy.

Liu et al [28] presented the multifold Bayesian Kernelization technique that can

distinguish AD from NC with a greater accuracy, but yielded poorer results in

the diagnosis of MCI-converter (MCIc) and MCI-non-converter (MCInc). Zhao

et al [29] introduced an enhanced Iterative Trace Ratio (iITR) algorithm for

resolving the Trace Ratio Linear Discriminant Analysis (TR-LDA) problem for

the diagnosis of dementia and accomplished better performance compared to the

Principal Component Analysis (PCA), Locality Preserving Projections (LPP),

and Maximum Margin Criterion (MMC).

Siddiqui et al [30] performed the extraction of the Master Features of the

images by making use of a fast Discrete Wavelet Transform (DWT), and

thereafter these distinguishing features are analyzed further by means of

Principal Component Analysis (PCA). Various subset sizes of the principal

feature vectors are given to five diverse decision models. The classification

models are inclusive of the J48 decision tree, K-Nearest Neighbour (KNN),

Random Forest (RF), and Least-Squares Support Vector Machine (LS-SVM)

with polynomial and radial basis kernels.

Lama et al [31] studied and compared AD diagnosis techniques employing

structural Magnetic Resonance (sMR) images for the discrimination AD, Mild

Cognitive Impairment (MCI), and Healthy Control (HC) subjects employing a

Support Vector Machine (SVM), an Import Vector Machine (IVM), and a

Regularized Extreme Learning Machine (RELM). The performance of these

classifiers for volumetric MR image data is compared with the help of ADNI

datasets. The experiments conducted on the ADNI datasets revealed that RELM

with the feature selection technique can considerably enhance the classification

accuracy of AD from MCI and HC individuals.

Luo et al [32] explained about an automated AD recognition algorithm, which is

on the basis of deep learning on 3D brain MRI. The algorithm makes use of a

Convolutional Neural Network (CNN) to complete AD recognition. It is distinct

in the fact that the three dimensional topology of brain is regarded to be

complete in AD recognition, leading to an accurate identification. The CNN

utilized in this work comprises of three sequential sets of processing layers, two


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completely connected layers and a classification layer. In the structure, each one

of the three groups is built with three layers, inclusive of a convolutional layer,

a pooling layer and a normalization layer.

Long et al [33] introduced a machine learning technique for discriminating the

patients with AD or Mild Cognitive Impairment (MCI) from the healthy elderly

subjects and for predicting the AD conversion in MCI patients by calculating

and evaluating the regional morphological variations of brain between different

groups. The new technique provided an accuracy of 96.5% in distinguishing

mild AD from healthy elderly providing the whole-brain gray matter or

temporal lobe to be ROI, 91.74% in discriminating progressive MCI from the

healthy elderly people and 88.99% in categorizing progressive MCI against the

stable MCI with amygdala or hippocampus as ROI.

3. Proposed Methodology

The major contribution of the work is to improve the MRI classification result

by selecting the optimal features from the given images. The Enhanced

Gaussian Bat Algorithm and Doppler Effect (EGBA-DE) is used for

preprocessing step for removing the noise features. Then the feature extraction

is performed by using Haar Wavelet Transform (HWT) method. Then Gaussian

Membership Based Fuzzy C-Means (GMFCM) is proposed for feature

selection; it computes the cluster center using Gaussian weights with fuzzy

membership function. The similarity between two features are computed using

Euclidean distance function, then irrelevant features in the feature set is

removed from original feature subset. Then Supervised Hybrid Fuzzy Neural

Network (SHFNN) is used for classifying the features from the feature selection

phase. It gives improved classification outcome while matched up with the other

techniques.

Image Denoising - Enhanced Gaussian Bat Algorithm and Doppler Effect (EGBA-DE)

For medical image processing, noise is one among the major issues that un

enviably disgraces medical images. Image denoising eliminates noise from

image, when preserving its quality. Noise removal is used to several medical

images improvements. In digital images, there are diverse kinds of noise.

Certain categories contain salt and pepper noise, speckle noise, Gaussian noise,

Rician noise, and so on. Speckle noise is detected in ultrasound images,

Gaussian noise frequently seems in natural images, Rician noise have an

emotional impact on MRI when random noise could seem in any kind of

images. Pattern of the noise based on its source [34]. In case of digital images, it

is very hard to eliminate the noise, which contains a low frequency as it is hard

to differentiate low frequency noise from the real signal [35]. Owing to bad

instruments in image processing or interface, Generation of noise could arise.

Noise on digital images might be gotten by compression, error in transmission

or some other aspects. By numerous kinds of noise, MRI images are disgraced

[36].


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Gaussian noise is a general kind of noise in MRI images. Noise is distributed

evenly over the image, at every pixel of the image, random value from Gaussian

distribution was included. Gaussian distribution of noise is stated here:

(1)

Here g is gray level, m is known as average or mean of the function and is

called the standard deviation. Gaussian noise is a statistical noise that

comprises the probability density function of the normal or Gaussian

distribution. Noise values were gotten from the Gaussian distribution. Gaussian

noise is the noise with a Gaussian amplitude distribution [37]. From complex

Gaussian noise, Rician noise is gotten, which is one more noise that is capable

of disgracing magnetic resonance images. This noise had probability density

function for intensity x provided by the subsequent equation:

(2)

Rician noise is based upon the signal in MRI that is not zero-mean. In bright

areas, distribution of Rician noise is nearer to Gaussian. Speckle noise called

Multiplicative noise seems in diverse imaging systems and as well in MRI

images. It seems in images, which is capable of giving valuable diagnostic info

regarding the disease in the human body. This noise is produced by errors in

data transmission and it is dependent upon gamma distribution such that it is

well-defined by the subsequent equation:

(3)

Denoising method remove the noise by utilizing a filter. Techniques of filtering

could contain a defect, with the intension that high-frequency signals are

removed from the component producing blurry edges in MRIs. Eliminating the

random noise in the MRIs by means of an EGBA-DE is elucidated in the sub

sequent‟s.

Bat Algorithm (BA)

Bat algorithm was carried out under motivation of echolocation conduct of bats

[38]. Usually the bats are carried out dependent upon the three rules:

1. Each and every bat utilizes echolocation to intellect distance, and they as

well „know‟ the variance amid food/prey and background barriers in

certain magical manner;

2. Bats fly arbitrarily with velocity vi at position xi with a frequency fmin,

changing wavelength and loudness A0 to look for prey. They could

mechanically regulate the wavelength (or frequency) of their produced

pulses and fine-tune the rate of pulse emission , based upon the

proximity of their target;


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3. Though the loudness could change in numerous means, presume that the

loudness changes from a large (positive) A0 to a least constant value

Amin. In Figure 4.2., the flowchart of the Bat Algorithm is depicted.

In BA variation of the frequency as well as wavelength mainly increase the

outcomes. Rather than carrying out linear implementation, interesting extension

would be to utilize Gaussian distribution for wavelength as well as frequency

variations. An additional main significant problem of the present BA is the time

delay amid pulse emission as well as the echo bounced happens. With the aim

of resolving this issue additional natural expansion to the present BA will be to

utilize the directional echolocation and Doppler Effect that might bring about

improving image enhancement outcomes.

Enhanced Gaussian Bat Algorithm and Doppler Effect (EGBA-DE)

Gaussian distribution is the well-known distribution.

(4)

The mean is depicted as µ and the variance is denoted as ζ2 for frequency f.

The Standard Deviation is calculated in this manner:

(5)

Mean is an arithmetic average of the frequencies, computed by toting all the

frequencies and dividing by the total amount of frequencies

(6)

Movement of Virtual Bats

In the EGBA-DE, virtual bats are utilized. Describe the rules how their pixel

position and velocities vi in a d-dimensional image search space are brought-

up-to-date. The novel quality analysis solution and velocities at iteration

t are provided by

(7)

(8)

(9)

Here is known as a random vector derived from a uniform

distribution. is known as the present global best quality outcome that is

placed subsequently comparing all the solutions among teach and every n bats

at every iteration t. Since the product is the velocity increment, utilize (or

λi ) to regulate the velocity change when fixing the other factor λi (or ), based

upon the kind of the problem of interest. For the local quality analysis part, as

soon as a solution is chosen amongst the present best solutions, a novel quality

assessed solution for every bat is produced locally by means of random walk.

(10)


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Here ε is known as a random number that is derived from a uniform distribution

in [−1,1], when At = is the average loudness of all the bats at iteration t.

Loudness and Pulse Emission

Likewise, the loudness Ai and the rate ri of pulse emission must be brought up to

date consequently since the iterations go on. Since the loudness reduces as soon

as a bat has identified its ideal quality outcomes, when the pulse emission rate

rises, the loudness is selected as any value of convenience. For easiness, utilize

A0 = 1 and Amin = 0, presuming Amin = 0 denotes that a bat has identified the

prey and provisionally stop producing any sound.

(11)

(12)

Here α and γ are constants. For any 0 < α < 1 and γ > 0, have

as (13)

In the modest case, we could utilize α = γ, α = γ = 0.9 in the simulations. The

selection of parameters needs certain testing. Firstly, every bat must contain

diverse values of loudness as well as pulse emission rate, and this is attained by

randomization. Perhaps, the preliminary loudness is archetypally be around

[39], when the primary emission rate isabout zero, or any value ∈ [0,1] if

utilizing (13). Their loudness as well as emission rates are brought up to date

only when the novel solutions are enhanced and these bats are moving in the

direction of the ideal solution [40]. Dependent upon the Peak Signal To Noise

Ratio (PSNR), the fitness value of the Bat Algorithm is calculated. The

fundamental steps of the EGBA-DE are précised as the pseudo code depicted in

algorithm 1., dependent upon the above guesstimates and idealization.

Algorithm1. EGBA-DE Algorithm for image denoising in AD prediction

1. Input : Pi (i = 1,2,...,n) with number of pixels

2. Begin

3. Initialize a population of n bats Pi (i = 1,2,...,n) with number of pixels in the

image and velocity vi

4. Initialize frequencies , pulse rates and the loudness .

5. While (t <Max number of iterations)

1.1. Generate new optimal PSNR results by adjusting frequency new

frequency from equation (7), and updating velocities and

locations/solutions [(8) to (9)]

6. If (rand > ri)

6.1.Select a best PSNR quality results among the best PSNR values

6.2.Generate a local PSNR values around the selected best PSNR values

7. End if //step 6

8. Else

8.1. Generate a new PSNR values by flying randomly

9. if (rand < Ai & f(Pi) < f(Pi*))

9.1. Accept the new solutions


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9.2. Increase ri and reduce Ai

10. End if// step 9

11. Rank the bats and find the current best Pi∗

12. End while// step 5

13. End begin

Feature extraction- Haar Wavelet Transformation (HWT)

Haar Transform is the modest and fundamental transformation from the space

domain to a local frequency domain and could act as a sample for orthonormal

wavelet transforms [41]. When an image encompasses with N

elements; there is N/2 averages and N/2 wavelet coefficient values. In the first

half of the N element array, the averages are kept, and in the second half of the

N element, array coefficients are kept. The averages turn out to be the input for

the subsequent step in the wavelet computation. The Haar equations to compute

an average and a wavelet coefficient from an odd and even element in the

image are defined in this manner:

(14)

(15)

Steps for a 1D Haar transform of an array of N elements are in this way:

1. Identify the average of every pair of elements by utilizing Equation 1.

(N/2 averages)

2. Identify the difference amid every pair of elements and divide it by 2.

(N/2 coefficients)

3. Fill-up the first half of the array with averages.

4. Fill-up the second half of the array with coefficients.

5. Do again the process on an average part of the array till a single average

and a single coefficient are computed.

Feature selection- Gaussian Membership Based Fuzzy C-Means (GMFCM)

The Fuzzy C-Means (FCM) algorithm [42-43] is useful when the required

number of clusters is pre-determined; consequently, the FCM algorithm tries to

put each of the features to one of the clusters. What makes FCM algorithm is

different that it doesn‟t decide the absolute membership of features to a given

cluster; instead, it calculates the degree of membership that features will belong

to that cluster.

Hence, depending on the accuracy of the clustering that is required in practice,

appropriate tolerance measures be able to be put in place. Since the absolute

membership is not calculated, FCM be able to be extremely fast because the

number of iterations required to achieve a specific clustering exercise

corresponds to the required accuracy. In each iteration of the FCM algorithm,

the following objective function J [43] is minimized then the features are

selected or else the features are not selected:


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(16)

Here, N is the number of features, C is the number of clusters required, cj is the

centre vector for cluster j, and δij is the degree of membership for the ith

feature

point in cluster j. The norm, measures the similarity of the

feature point to the centre vector cj of cluster j. Note that in each iteration,

the algorithm maintains a centre vector for every clusters. These features are

computed as the weighted average of the features, in which the weights are

provided by the degrees of membership [44].

Degree of membership: For provided features , the amount of its

membership to cluster j is computed in this manner:

(17)

here, m is known as the fuzziness coefficient and the centre vector cj is

computed in this manner:

(18)

In equation (18), δij is known as the value of the amount of membership

computed in the prior iteration. The amount of membership for feature point i to

cluster j is set with a random value θij, 0 ≤ θij ≤ 1, then .

Fuzziness coefficient: In equations (17) and (18) the fuzziness coefficient m,

here , gauges the tolerance of the needed clustering. This value

identifies the amount of clusters could overlay with each other. The greater the

value of m denotes the superior of overlap amid clusters. Alternatively, the

greater the fuzziness coefficient the algorithm utilizes, a greater amount of data

points would fall ith

in a „fuzzy‟ band in which the amount of membership is

neither 0 nor 1, on the other hand somewhere amidst them.

Termination condition: The needed accurateness of the amount of

membership identifies the amount of iterations accomplished by the FCM

algorithm. The accurateness is computed by means of the amount of

membership from one iteration to the subsequent, considering the biggest of

these values crossways all feature points are taking all of the clusters. When

denote the measure of accuracy amid iteration k and k + 1 with compute its

value in this manner

(19)


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Here, and are correspondingly the amount of membership at iteration k

and k + 1, and the operator ∆, while provided a vector of values, yields the

biggest value in that vector. A novel clustering algorithm for a data objects

with diverse feature-weights that denote that data with features of diverse

weights must be clustered. In equation (16) the weight is calculated by

summation of all data points. In this research, we alter the amount of its

membership to cluster j dependent upon the weight value of the features i (wei)

and Gaussian membership function is computed in this manner:

(20)

Here ci and ζi are the centre and width of the ith

feature point vector,

correspondingly. Lastly most significant features are chosen by minimization of

the objective function (16).

Classification- Supervised HFNN approach

The Hybrid Fuzzy Neural Network (HFNN) model unites the anticipated

properties of Neural Network (NN) as well as fuzzy logic in order to create a

system that resolves the borders of NN and fuzzy logic. By means of the NN,

the HFNN model system learns inductively from the data. Fuzzy rules are taken

out from the trained NN and with the extracted rules, beforehand unseen

Alzheimer‟s disease is categorized as four classes for instance Alzheimer‟s

disease, Mild Alzheimer's disease, Huntington's disease and normal. The HFNN

model experiences two phases, called: training and implementation testing.

Figure 1 depicts the learning phase of the HFNN model. The classification

outcome is dependent upon the four classes for instance Alzheimer‟s disease,

Mild Alzheimer's disease, Huntington's disease and normal.

Figure 1: Architecture of the Learning Stage of the HFNN Model

Fuzzy Distribute Data

Configure HFNN

Start

Train NN

Evaluate NN

Optimal

NN?

Select Best Epoch

Tuning

Optimized

Re-Fuzzify/Re-

distribute data

Tune Input Fuzzy

Sets

Yes

No

No Yes

Input Fuzzy

Sets

Tuned?

Determine

Redundant Inputs

Prune Redundant

Inputs & Links

Yes

No

Extract Fuzzy

Rules

Retain Best k

rules

Predict Test Data

Using be best K

Rules

Convert Rules to

Human Readable

Form

End


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The diverse stages in the learning phase are: Fuzzification of the Data, NN

Learning, Fuzzy Sets Tuning, Pruning Input Variables, and Fuzzy Rule

Extraction & Evaluation.

a) Fuzzification of Data

The fuzzification of Alzheimer‟s samples are carried out and configured

physically. Each and every fuzzy sets are set before hand the training

Alzheimer‟s samples are put in into the neural net. The input variables are

divided by overlapping fuzzy sets and the membership functions are set

dependent upon the Alzheimer‟s samples. The requirement of membership

function is subjective and might change from diverse experts. On the other

hand, membership functions could not be allotted randomly. The amount of

membership functions is selected with the intension that the ensuing fuzzy rules

are simply readable and exact for categorizing alzheimer‟s samples.

b) Fuzzy Input Set Tuning

The tuning process of fuzzy input sets is carried out mechanically. Merely

inputs, which are continuous variables are tuned. Alteration on the limits of the

fuzzy members of a provided fuzzy set follows particular constraints. The

subsequent limitations are amended:

1. The fuzzy sets are kept overlapped with the adjacent fuzzy sets.

2. While bringing up to date the parameters, the parameters a, b, and c

must stay valid; that is to say l ≤ a ≤ b ≤ c ≤ u should all the time hold,

here [l, u] is known as the domain of the equivalent variable.

3. While keeping up to date the position parameters of the present fuzzy

member, the parameters of present member must not turn out to be

lesser than the equivalent parameters of the left neighbor or greater than

the equivalent parameters of the right neighbor.

4. The parameter „a‟ of the leftmost member and the parameter c of the

rightmost member stay static.

5. The parameter b of the present fuzzy member being brought up to date

must not turn out to be superior to the parameter c of the right member

or smaller than the parameter c of the left member. For each error back

propagated from the output to input fuzzy sets, the input fuzzy sets,

which are continuous variables, are attuned with the intention of

decreasing the error. Subsequent to the reasonable amounts of change

were done with the input fuzzy sets and up till now the error doesn‟t go

down, the tuning had attained its optimum. As soon as the fuzzy input

sets are tuned, the NN is re-trained to an optimum accurateness for the

freshly tuned fuzzy input sets.

c) Pruning the Input Variables or Fuzzy Sets

Pruning or elimination of redundant input variables would increase the

readability of a fuzzy rule base taken out for the period of the learning phase.

Elimination of the redundant input fuzzy sets would make the model simpler.

Pruning methods are amended from NN in which, examinations are made for its


286

parameters that is to say weights or neurons for identifying how the error will

modify when the parameter is eliminated [45]. The fuzzy sets, which signify

numerous alzheimer‟s samples variables in all probable combinations, are

detached and the network classification performance is assessed. The process in

finding redundant alzheimer‟s samples variables is carried out by systematically

facilitating or inactivating the inputs. While an alzheimer‟s samples are

facilitated, its contribution is agreed into the NN. On the other hand while an

input is inactivated, its value is clogged in the NN. It is as if the alzheimer‟s

samples don‟t be present. With all the probable grouping of enabled and

inactivated alzheimer‟s samples, the prediction accurateness of every

combination is recorded.

d) Fuzzy Rule Extraction and Evaluation

All likely fuzzy rules are taken out from the pruned system. Each potential

mixture of the fuzzy inputs is taken as a rule. This rule is being assessed by NN

whether the combination outcomes as four classes for instance Alzheimer‟s

disease, Mild Alzheimer's disease, Huntington's disease and normal. The rules

are drawn from distinctive combinations of the enabled fuzzy inputs. There are

no two rules, which have accurately the identical mixture of the enabled fuzzy

inputs. Therefore, there are no conflicting rules that come from these groupings.

The last rules for the rule base are chosen from the extracted rules by

calculating the performance of every rule. Rules, which are just accountable for

static amounts of classifications, might be removed. Just an amount of k best

rules are stored [45]. Here, k is known as a number identified by the no of rules

drawn from particular mixture of input variables. „k‟ is relative with the no of

input variable and fuzzy member for each input variable. Specified these

aspects, k is identified by the no of rules, which contain “hits” no less than

twice in the training set. Every mined fuzzy rule is rated by counting the times it

is utilized or “hit” by the training samples. Each and every rule, which were not

utilized were removed from the list of finest k rules that were utilized just as

soon as are removed from the list. The best k rules describe the prediction

model.

e) Fuzzy Logic Implementation Stage of HFNN Model

The optimized fuzzy sets attained for the period of the learning phase are

utilized in the implementation phase. The test samples are initially fuzzified via

these enhanced fuzzy sets beforehand these input are inferred with the fuzzy

rule base. As soon as the rules are assessed and compiled, every fuzzified input

is matched up with the compiled rules and is categorized, as depicted in Figure

2.


287

Figure 2: Architecture of the Fuzzy Logic Implementation Stage of the

HFNN Model

In case corresponding rule is not identified, the alzheimer‟s samples are

categorized or uncategorized. Or else, alzheimer‟s samples are categorized as

appropriately categorized or misclassified.

4. Results and Discussion

In this section, performance of proposed SHFNN classifier is evaluated. The

experiments were carried out on the MATLAB platform. The datasets includes

T2-weighted MRI in axial plane and 256 256 in-plane resolution, which were

obtained from the website of Harvard Medical School (URL:

http://med.harvard.edu/AANLIB/), OASIS dataset (URL: http:// www.oasis-

brains.org/), and ADNI dataset (URL: http://adni.loni.uc- la.edu/). The

proposed SHFNN classifier is compared with the existing Discrete Cosine

Transform (DCT) with Artificial Neural Network (ANN) and Support Vector

Machine (SVM) classifier. In the classification result is based on four classes

such as Alzheimer‟s disease, Mild Alzheimer's disease, Huntington's disease

and normal. The confusion matrix results of the four classifiers such as the

SVM, DCT-ANN , DWT-FNN and SHFNN classifiers is discussed in table 2,

3, 4 and 5 respectively. Doing so, the following standard definitions are

obtained: 1) True Positives (TP): predicts class as class. 2) True Negatives

(TN): predicts non-class as class. 3) False Positives (FP): predicts non-class as

non-class. 4) False Negatives (FN): predicts class as non-class. As a

consequence, the following standard definitions are obtained:

Fuzzifer

Fuzzy Rules

Inference

Crisp Inputs Crisp Output

Fuzzification of the Data Fuzzy System Classification of the

DATA


288

(21)

(22)

(23)

Peak Signal To Noise Ratio (PSNR)

It is the ratio between the utmost possible powers to the power of corrupting

noise is given as PSNR. It considerably has an effect on the fidelity of its

representation. It can be also observed that it is the logarithmic function of peak

value of image and Mean Square Error (MSE).

(24)

Mean Square Error (MSE)

Mean Square Error (MSE) of an estimator is to quantify the difference between

an estimator and the true value of the quantity being estimated.

(25)

The proposed DWT with FNN classifier is compared with the existing Discrete

Cosine Transform (DCT) with Artificial Neural Network (ANN) and Support

Vector Machine (SVM) classifier.

In the classification result is based on four classes such as Alzheimer‟s disease,

Mild Alzheimer's disease, Huntington's disease and normal. To assess the

classifier performance, count the number of True Positive, True Negative, False

Positive (actually negative, but classified as positive), False Negative (actually

positive but classified as negative) samples and form a confusion matrix (Table

1) as given below:

Table 1: Confusion Matrix

True Class

Classifier output

Conditions Positive Negative

Positive TP FP

Negative FN TN

Table 2: Confusion Matrix for SVM Classifier

True Class

Classifier output


Positive 51 10

Negative 9 30

Column sum 60 40


289

Table 3: Confusion Matrix for DCT-ANN Classifier

True Class

Classifier output


Positive 51 8

Negative 9 32

Column sum 60 40

Table 4: Confusion Matrix for DWT-FNN Classifier

True Class

Classifier output


Positive 53 6

Negative 7 34

Column sum 60 40

Table 5: Confusion Matrix for SHFNN Classifier

True Class

Classifier output


Positive 55 4

Negative 5 36

Column sum 60 40

Some preliminary results, in terms of sensitivity, specificity, and accuracy are

reported in Table 6 for the MR brain images.

Table 6: Overall Comparison Results(Proposed SHFNN model)

Classification

Methods

Accuracy (%) Error Rate (%) Specificity (%) Sensitivity (%)

SVM 81 19 75 85

DCT - ANN 83 17 80 85

DWT - FNN 87 13 85 88.33

SHFNN 91 9 90 91.67

Figure 3: Accuracy Vs Methods (Proposed SHFNN Model)

Figure 3 shows the evaluation comparison of the accuracy in terms of accuracy

(%). The figure 3 shows that the proposed SHFNN classifier has provides

0

10

20

30

40

50

60

70

80

90

100

Classification results

Accura

cy (

%)

SVM

DCT-ANN

DWT-FNN

SHFNN


290

higher average accuracy of 91% which is 4%, 8% and 10% higher when

compared to other DWT-FNN, DCT-ANN and SVM classifiers.

Figure 4: Specificity Vs Methods (Proposed SHFNN Model)

Figure 4 shows the comparison results in terms of specificity metric. SHFNN

classifier has provides higher average specificity of 90% which is 5%, 10%, and

15% higher when compared to other DWT-FNN, DCT-ANN and SVM

algorithms.

Figure 5: Sensitivity Vs Methods (Proposed SHFNN Model)

Figure 5 shows the comparison results in terms of sensitivity metric. SHFNN

classifier has provides higher average sensitivity of 91.67% which is 3.34%,

6.67%, and 6.67% higher when compared to other DWT-FNN, DCT-ANN and

SVM algorithms.

0

10

20

30

40

50

60

70

80

90

100


Specific

ity (

%)

SVM

DCT-ANN

DWT-FNN

SHFNN

0

10

20

30

40

50

60

70

80

90

100


Sensitiv

ity (

%)

SVM

DCT-ANN

DWT-FNN

SHFNN


291

Figure 6: Error Rate Vs Methods (Proposed SHFNN Model)

Figure 6 shows the comparison results in terms of error rate metric. SHFNN

classifier has provides higher average error rate of 9% which is 4%, 6%, and

10% lesser when compared to other DWT-FNN, DCT-ANN and SVM

algorithms.

Figure 7: PSNR Vs Methods (Proposed SHFNN Model)

From Figure 7, it can be proved that the proposed classifier has provides higher

PSNR (dB) of 38.48 dB, higher than 4.38 dB, 3.03 dB and 2.02 dB when

compared to other DWT-FNN, DCT-ANN and SVM algorithms. The table

values of PSNR and MSE are discussed in table 7.

0

2

4

6

8

10

12

14

16

18

20


Err

or

Rate

(%

)

SVM

DCT-ANN

DWT-FNN

SHFNN

0

5

10

15

20

25

30

35

40


PS

NR

(dB

)

SVM

DCT-ANN

DWT-FNN

SHFNN


292

Figure 8: MSE Vs Methods (Proposed SHFNN Model)

From Figure 8, it can be proved that the proposed SHFNN classifier has

provides lesser MSE of 0.58 lesser than 0.07, 0.12 and 0.16 when compared to

other DWT-FNN, DCT-ANN and SVM algorithms. The table values of MSE

are discussed in table 7.

Table 7: PSNR and MSE Vs methods (Proposed SHFNN model)

Methods PSNR(dB) MSE

SVM 34.06 0.74

DCT - ANN 35.41 0.7

DWT - FNN 36.42 0.65

SHFNN 38.44 0.58

5. Conclusion and Future Work

In this paper Gaussian Membership Based Fuzzy C-Means (GMFCM) is

proposed for features selection. To improve the MRI classification result by

selecting the optimal features from the given images. Initially Enhanced

Gaussian Bat Algorithm and Doppler Effect (EGBA-DE) is used for

preprocessing step for removing the noise features. Then the feature extraction

is performed by using Haar Wavelet Transform (HWT). After feature selection

is performed by GMFCM. GMFCM based clustering algorithm, it computes the

cluster center using Gaussian weights with fuzzy membership function and

classification by SHFNN framework. Finally Supervised Hybrid Fuzzy Neural

Network (SHFNN) is used for classifying the features from the feature selection

phase. It provides better classification result. In the future, the various Scale

Invariant Feature Transform (SIFT) images can be improved by using hybrid

optimization based FS and transformation with advanced classification

strategies. In the future work new ensemble based classification algorithms are

introduced for detection of AD which increases detection results.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


MS

E

SVM

DCT-ANN

DWT-FNN

SHFNN


293

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enhanced gaussian bat algorithm and doppler effect (egba

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