Classification of Alzheimer’s Disease, Mild Cognitive Impairment,

and Normal Controls with Multilayer Perceptron Neural Network

and Neuropsychological Test Data

Ibrahim Almubark

, Samah Alsegehy

, Xiong Jiang

and Lin-Ching Chang

Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, U.S.A.

Department of Computer Science and Engineering, Penn State University, University Park, PA, U.S.A.

Department of Neuroscience, Georgetown University Medical Center, Washington, DC, U.S.A.

Keywords: Multilayer Perceptron Neural Network, Alzheimer’s Disease, Mild Cognitive Impairment,

Neuropsychological Test.

Abstract: Recent advances in machine learning have shown outstanding performances in biological and medical data

analysis to assist for early detection, diagnosis, and treatment of diseases. Alzheimer's disease (AD) is a

neurodegenerative disease and the most common cause of dementia in older adults. In this study, multilayer

perceptron (MLP) neural networks are developed to classify AD, Mild Cognitive Impairment (MCI), and

Cognitive Normal (CN) subjects based upon the data from standard neuropsychological tests. Three

neuropsychological tests from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database,

Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog), Mini-Mental State Examination

(MMSE), and Functional Activities Questionnaire (FAQ), were used to train MLP neural networks. We first

build three MLP models that can classify AD vs. CN, AD vs. MCI, and MCI vs. CN. We then construct a 3-

way MLP classifier to classify AD vs. MCI vs. CN. Finally, we propose a cascade 3-way classification method

to further improve the model performance. Using the neuropsychological test data from ADNI database, our

result shows the pairwise MLP models (i.e., AD vs. CN, AD vs. MCI, and MCI vs. CN) have the accuracy of

99.760.48, 89.643.94, and 90.812.91, respectively. The multi-class MLP model has an average accuracy

of 84.283.66, and the proposed cascaded MLP approach further improves the performance of the multi-class

classification with an average accuracy of 86.263.15.

1 INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative

disease and the most common cause of dementia in

older adults. AD pathologies often start 5, 10, or even

20 years before symptoms appear (Alzheimer’s

Association, 2020). Symptoms usually start with

difficulty remembering new information. Since this

condition is also common with the normal aging

process, distinguishing between early AD and normal

aging can be difficult (Holtzman et al., 2011). In

advanced stages, symptoms include confusion, mood

and behavior changes, and inability to care for one’s

self and perform basic life tasks. AD is ultimately

fatal (Taylor et al., 2017). While significant progress

has been made, there are yet no proven effective

treatments for AD. As a result, there is increasing

For the Alzheimer’s Disease Neuroimaging Initiative

pressure to develop techniques to assist in the

diagnosis of early AD, as early intervention may be

most effective in treating or slowing disease progress.

In addition, early diagnosis may provide useful

information for the development of more effective

treatments (Fiandaca et al., 2014).

Three groups of subjects are included in this

machine learning classification study: Cognitively

Normal (CN) older adults, Mild Cognitive

Impairment (MCI) due to AD, and AD. A CN subject

has no signs of cognitive impairment other than age-

related normal decline. A subject converts from CN

to MCI when symptoms become mild yet noticeable

to the patient or close family members/friends. MCI

is a transitional stage between CN and AD, and is the

earliest clinically detectable stage of progression

towards dementia or AD (Sperling et al., 2011).

Approximately 15-20% of seniors age 65 or older

Almubark, I., Alsegehy, S., Jiang, X. and Chang, L.

Classiﬁcation of Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Controls with Multilayer Perceptron Neural Network and Neuropsychological Test Data.

DOI: 10.5220/0010143304390446

In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020), pages 439-446

ISBN: 978-989-758-475-6

439

have MCI (Alzheimer’s Association, 2020). Patients

diagnosed with MCI are of higher risk for developing

AD or other types of dementia, and are therefore

given special attention (Ansart et al., 2020). AD is the

last stage in this progression.

In recent years, the world has seen many major

breakthroughs in the field of healthcare because of the

rapid proliferation of large biomedical datasets,

concurrent with advances in machine learning,

especially in deep learning (Esteva et al., 2017).

These advances have opened new avenues for the

development of diagnostic tools to assist early

detection of AD. Recently, several studies have

focused on the detection of different cognitive groups

by utilizing various types of biomedical data

including brain imaging data (Pellegrini et al., 2018),

cerebrospinal fluid (CSF) specimens (Jack et al.,

2018; Shaffer et al., 2013), and behavioral data from

speech (Fraser et al., 2016; Nagumo et al., 2020),

body movement (Khan & Jacobs, 2020), and

neuropsychological tests (Grassi et al., 2019; Kang et

al., 2019; Lee et al., 2019).

Standard neuropsychological tests are typically

used in the diagnosis of cognitive impairment in

individuals with MCI, AD, or other neurological

conditions (Seo, 2018). These tests are less

expensive, easy to conduct, and widely available

compared to medical imaging. The scores from these

tests can measure normal and abnormal cognitive and

behavioral functions and provide useful features to

machine learning methods for the early detection of

AD (Anastasi & Urbina, 1997). Repeated assessment

with these tests is frequently used to evaluate changes

in a treated person’s condition over time (Harvey,

2012). The existence of multiple cognitive deficits

indicates that a combination of neuropsychological

tests from different domains may improve clinical

diagnosis accuracy (Harvey, 2012; Storey & Kinsella,

2007; Yeatts et al., 2018).

In this paper, we present fully connected

multilayer perceptron (MLP) networks to perform

binary classification between different cognitive

groups (i.e., AD vs. CN, AD vs. MCI, and MCI vs.

CN) using the baseline visit data from three

neuropsychological tests in the Alzheimer’s Disease

Neuroimaging Initiative (ADNI) database

(http://adni.loni.usc.edu/). A direct MLP based 3-way

classification (i.e., AD vs. MCI vs. CN) is also

developed. Additionally, we propose a MLP based

cascading approach to further improve the multi-class

classification performance.

The paper is organized as follows. Section 2

includes descriptions of data used in this study, data

pre-processing, experimental design, and proposed

methods. Section 3 includes results from several

multilayer perceptron models. Section 4 includes a

summary of results, discussion, and future research

directions.

2 MATERIALS AND METHODS

2.1 Data

2.1.1 ADNI Database

The data used in this study was obtained from the

ADNI database. ADNI is a longitudinal multicenter

study designed to develop clinical, imaging, genetic,

and biochemical biomarkers for the early detection

and tracking of AD progression. The ADNI project

draws on a broad range of academic institutions and

private corporations, with subjects recruited from

over 50 sites across the U.S. and Canada. The project

began in 2003 and has been extended to different

phases. The first phase of ADNI (ADNI-1) was

completed in 2010 and has been followed by ADNI-

GO, ADNI-2, and ADNI-3. These four protocols

have recruited over 1900 adults, with ages from 55 to

90, and consist of elderly CN controls, people with

MCI, and people with AD. The follow-up duration of

each group is described in the protocols for ADNI-1,

ADNI-GO, ADNI-2, and ADNI-3. For detailed

information, please see (www.adni-info.org).

2.1.2 Subjects

In this study, we used the baseline visit data from a

total of 808 subjects at the initial project period

(ADNI-1), including 188 AD, 391 MCI, and 229 CN.

The enrolled subjects were between 55-90 (inclusive)

years of age, in good general health, having a partner

who is able to provide an independent assessment of

the subject’s functioning, having at least 6 grades of

education or work history, and were fluent in English

or Spanish. All subjects and their study partners

completed the informed consent process, and study

protocols were reviewed and approved by the

Institutional Review Board at each ADNI data

collection site (Petersen et al., 2010). Table 1 shows

the characteristics of the AD, MCI, and CN subjects

included in this study. The mean test score was

computed by averaging the scores from all the

questions in one test.

NCTA 2020 - 12th International Conference on Neural Computation Theory and Applications

440

Table 1: Characteristics of subjects at their baseline visit recruited during ADNI-1.

Characteristic AD (n=188) MCI (n=391) CN (n=229) p-value

Age, years

74.97.4 74.4



7.3 75.4



5.0

0.167

Education, years

14.73.1 15.6



3.0 16.1



2.8 3.9810

-5

Sex, male/female 97/91 255/136 119/110

5.4310

-4

ADAS-Cog score

28.97.6 18.6



6.3 9.5



4.2 4.9510

-145

MMSE score

23.42.0 27.0



1.8 29.1



0.9 3.0810

-164

FAQ score

13.06.8 3.8



4.5 0.1



0.6 4.0710

-28

Values are shown as mean  standard deviation or gender ratios. The p-values for differences between AD, MCI, and CN

are based on t-test. ADAS-Cog = Alzheimer's Disease Assessment Scale-Cognitive subscale; MMSE = Mini-Mental State

Examination; FAQ = Functional Activities Questionnaire.

2.1.3 Neuropsychological Data

The itemized scores of three neuropsychological tests

were used, which include Alzheimer’s Disease

Assessment Scale-Cognitive subscale (ADAS-Cog)

(Rosen et al., 1984), Mini-Mental State Examination

(MMSE) (Folstein et al., 1975), and Functional

Activities Questionnaire (FAQ) (Pfeffer et al., 1982).

Table 2 details the cognitive/daily functions

associated with individual tests. In total, there are 13,

30, and 10 questions from ADAS-Cog (note that Q13

was not used by default), MMSE, and FAQ,

respectively. The score of each individual question is

treated as a feature in our machine learning task,

resulting in a total of 13, 30, and 10 features for the

ADAS-Cog, MMSE, and FAQ datasets. The three

neuropsychological tests are widely used to assist

cognitive impairment in AD. The most prominent

feature of AD is memory impairment. Therefore,

ADAS-Cog and MMSE tests were used in this study.

ADAS-Cog and MMSE also test global cognitive

function as well as several domains other than

memory (Casanova et al., 2013). Information on

function from the FAQ test was also included as

functional changes begin to appear earlier in the

dementia process (John et al., 2016).

Table 2: Neuropsychological tests used in this study.

Neuropsychological Tests

ADAS-Cog Registration (3)

Q1. Word recall Attention and calculation (5)

Q2. Word recognition Recall (3)

Q3. Object naming Language (8)

Q4. Recall test instructions Visual construction (1)

Q5. Orientation FAQ

Q6. Commands Q1. Manage finances

Q7. Clarity of language Q2. Complete forms

Q8. Comprehension Q3. Shop

Q9. Word finding Q4. Perform games of skill or hobbies

Q10. Ideational praxis Q5. Prepare hot beverages

Q11. Constructional praxis Q6. Prepare a balanced meal

Q12. Delayed word recall Q7. Follow current events

Q14. Number cancellation Q8. Attend to TV, books, or magazines

MMSE Q9. Remember appointments

Orientation (10) Q10. Travel out of the neighborhood

2.2 Experimental Design for

Classification

Our multilayer perceptron (MLP) neural networks

were developed using (1) the original set of features

from each neuropsychological test (i.e., 13 from

ADAS-Cog, 30 from MMSE, and 10 from FAQ), and

(2) the combined-test of 53 features from three

neuropsychological tests. We first trained three

binary MLP models to perform binary classification

between different cognitive groups for both the

individual tests and the combined-test: (Case 1) AD

vs. CN, (Case 2) AD vs. MCI, and (Case 3) MCI vs.

CN. We also trained a MLP network with multi-class

classification network, i.e., AD vs. MCI vs. CN (case

4). Furthermore, we proposed a MLP based cascading

approach to further improve the multi-class

classification performance (case 5).

The proposed cascade MLP method is composed

of 2 steps. In the first step, we classified CN vs. (AD

+ MCI) with a MLP network. Then we trained

another MLP network with the predicted AD or MCI

samples from the first step to classify AD vs. MCI

(step 2). Note that the true CN samples misclassified

in step 1 as (AD + MCI) will participate in the second

step and they will be counted as misclassified CN

regardless of their predicted results in step 2. On the

other hand, the true MCI or AD samples misclassified

as CN will not participate in the training in step 2, but

will be counted in the final 3×3 confusion matrix

along with the correctly classified CN samples after

step 1.

The implementation was carried out using Python

and related libraries including Scikit-learn, Pandas,

Numpy, TensorFlow, and Keras (Chollet, 2015;

Pedregosa et al., 2011).

2.3 Data Pre-processing

The original baseline visit dataset in ADNI-1 consists

of 200 AD, 400 MCI, and 229 CN. We excluded 12

subjects from AD and 9 subjects from MCI before our

data analysis since answers to some questions were

Classiﬁcation of Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Controls with Multilayer Perceptron Neural Network and

Neuropsychological Test Data

441

not recorded (i.e., missing values in some features).

Therefore, the dataset (n=808, AD=188, MCI=391,

CN=229) used in our study does not have any missing

values. In statistics it is a common practice to drop

cases with missing values as long as the sample size

is sufficiently large and the number of dropped cases

does not exceed 5% of the overall sample. In this

study, the number of missing value subjects was

relatively small (12 out of 200 for AD and 9 out of

400 for MCI). Therefore, we elected to drop without

replacement the twenty-one samples with missing

value. Feature normalization was performed by

standard scaling with a zero mean and standard

deviation equal to one.

2.4 Data Partitioning

Cross-validation (CV) with stratified K-Fold was

used to evaluate the predictive model. Data was

divided into 5 disjoint subsets with consistent ratios

between classes in each fold. Eighty percent of the

data was used in training and 20% of the data was

used for testing in each fold.

2.5 Multilayer Perceptron (MLP)

Neural Network

Multilayer perceptron (MLP) is a feed-forward

artificial neural networks that uses back-propagation

to update weights (Marsland, 2015). The neurons are

connected to later layers in a way that pushes

information from the input, through hidden layer(s),

to the output layer. MLP leverages a layered

architecture of stacked perceptrons to solve complex,

often supervised, problems. MLP can approximate

non-linear functions for both classification and

regression (Joshi, 2020).

In this paper, we developed multiple fully

connected MLP networks to classify different

cognitive groups (AD, MCI, and CN) using data from

three neuropsychological tests. Figure 1 shows a 3-

layer MLP network that can be used to classify AD

and CN subjects. As an example, if the combined-test

data was used, the resulted MLP model will have 53

nodes in the input layer, 6 nodes in the hidden layer,

and 1 node in the output layer. The Rectified Linear

Unit (ReLU) was selected as the activation function

for the input and hidden layers (Xu et al., 2015). The

sigmoid function and the binary cross-entropy loss

function were used for the binary classifications. The

softmax function and the multi-class cross-entropy

were used for the multi-class classification (Sharma,

2017). The Adaptive Movement Estimation (Adam)

was used as our optimizer to tune the network during

the training (Kingma & Ba, 2014).

The cross-entropy loss function defined in

equation (1) is used to quantify MLP model errors:

ℒ

𝑤



𝑝



𝑐|𝑥





log𝑞



𝑐|𝑥









𝜆



||𝑤

,





(1)

where c, i, k are indices for classes, samples, and

layers, respectively; w

is the class weight for class c;



𝑐|𝑥





is the true probability for sample x

to be

assigned to class c; q



𝑐|𝑥





is the predicted

probability for sample x

to be assigned to class c;

𝜆



is the regularization strength for layer k; and w

k,k-1

are the weights between the (k-1)

and the k

layer.

A larger class weight on class c will penalize more if

samples in class c are misclassified. We also

considered the probability threshold as another hyper-

parameter. For example, a sample can be predicted to

have a probability of 0.45 to be class 1 and 0.55 to be

class 0, if we set this probability at default 0.5, this

sample will be classified to class 0. However, one

could set the probability threshold to 0.4 instead, the

sample will then be classified to class 1. In our

training, we also included the class weights and

probability thresholds as hyper-parameters to avoid

the imbalance issue between the model sensitivity and

specificity.

Figure 1: A 3-layer MLP network. Circular nodes represent

artificial neurons. Arrows represent connection from a

neuron output to a neuron input.

NCTA 2020 - 12th International Conference on Neural Computation Theory and Applications

442

To avoid overfitting, we have employed L2

regularization in the hidden layers as shown in

equation (1). The regularization strength for each

layer λ



was also tuned as a hyper-parameter. We also

applied the EarlyStopping function in Keras by

observing the loss function on the validation set. If the

loss function reduction was less than (110



) for

5 consecutive epochs, the training will be stopped.

Learning rate (or shrinkage factor) was adjusted

based on the reduction rate of loss function to train

the network more efficiently, and the

ReduceLROnPlateau function in Keras was used to

observe the loss function reduction. If the loss

function reduction was less than (110



) for 10

consecutive epochs, the learning rate would be

reduced to one tenth of the previous learning rate or

the minimum learning rate. The initial learning rate

was set to 0.01 and the minimum value is (510



The early stopping and the learning rate shrinkage

helped our training process with a positive impact in

the classification performance.

2.6 Performance Evaluation

To evaluate the performance of the classifiers,

sensitivity, specificity, and accuracy were calculated

for each model. Sensitivity measures the ratio of

actual positive subjects to the total numbers of

subjects identified by the test as being positive, i.e.,

true positive rate. Specificity measures the ratio of

actual negative subjects to the total number of

subjects testing negative, i.e., true negative rate.

Accuracy is the ratio of correctly classified subjects

to the entire set of subjects. In other words,

sensitivity, specificity, and accuracy are described in

terms of TP (True Positives), TN (True Negatives),

FN (False Negatives), and FP (False Positives), and

defined in equations (2), (3), and (4), respectively.

Sensitivit



TP  FN

(2)

Specificit



TN  FP

(3)

Accurac



TP  TN

TP  TN  FP  FN

(4)

To illustrate the diagnostic ability of a classifier,

we also calculated the Area Under the Curve (AUC)

from the Receiver Operating Characteristic (ROC)

curve. The AUC was also used as the target metric

during hyper-parameter tuning. An algorithm with an

AUC closer to 1, indicating a near perfect

performance, is considered as the more reliable

predictive model.

3 RESULTS AND DISCUSSION

Table 3 summarizes the performance of binary and 3-

way classification using each individual

neuropsychological test (i.e., ADAS-Cog, MMSE,

and FAQ) as well as a combination of these three tests

to discriminate different cognitive groups. The

default class weights was set to 1:1 for binary

classifier or 1:1:1 for 3-way classifier, and the default

probability threshold was set to 0.5. As shown in

Table 3, the model using the combined features from

three tests outperformed the models using each single

test. The classification of AD vs. CN subjects had

very high accuracies (98%~100%) in all models. This

indicates the proposed MLP method using

neuropsychological test data is very effective in

classifying AD and CN. The classification accuracy

of MCI vs. CN was 77%~82% when using a single

test, but reached 90% when using the combined tests.

Classification between AD and MCI was our most

challenging task. Although the overall accuracy was

acceptable (80%~84%), the sensitivity was very poor

(47%~69%) when using a single neuropsychological

test. However, the sensitivity significantly improved,

to 81.38%, when using the combined-test.

In Table 4, we demonstrated that class weights

and probability thresholds could be used to improve

the model performance and obtain a more balanced

sensitivity and specificity ratio. By tuning the class

weights and probability thresholds in training of MLP

networks, the sensitivity as well as the accuracy can

be further improved. For example, the sensitivity of

AD vs. MCI improved from 46.81% to 79.26% with

ADAS-Cog test (data not shown) and from 81.38% to

91.49% with the combined-test. The accuracy of AD

vs. MCI vs. CN was improved from 82.43% to

84.28%. While this MLP 3-way classification

accuracy is notably lower than the binary

classifications, it outperformed other existing

methods. For example, its accuracy was 22% higher

than Lee’s model (Lee et al., 2019).

Table 5 shows the performance of the MLP model

with the cascade approach using the combined-test.

The class weights and probability thresholds were

tuned to obtain optimal model performance. This new

model further improved the results compared to the

direct 3-way classification (Tables 3 and 4) in terms

of sensitivity, specificity, and accuracy.

Standard neuropsychological tests are often

incorporated into regular physical examinations for

Classiﬁcation of Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Controls with Multilayer Perceptron Neural Network and

Neuropsychological Test Data

443

seniors, this study demonstrated that early screening

of AD is possible when using these tests with neural

networks. The proposed methods are effective to

classify different cognitive groups, and do not require

the medical procedures that are presently more

expensive, invasive, or not offered in many clinical

settings. These medical procedures include

neuroimaging, cerebrospinal fluid (CSF), and genetic

testing. This study also showed that a combination of

a variety of neuropsychological tests and assessments

used for AD diagnosis improved the accuracy of

clinical diagnosis. Lastly, this study points to the

potential for MLP neural network enabled classifiers

in discriminating between AD progression classes.

Table 3: The classification performance of binary and 3-way multilayer perceptron (MLP) networks using data from a single

neuropsychological test and the combined-test. The default class weights (1:1 or 1:1:1) and probability thresholds (0.5) were

used in each model.

Dataset Classification Case SEN% SPE% ACC% AUC

ADAS-Cog

(13)

AD vs. CN 93.62 98.69

96.04  1.71

0.994

AD vs. MCI 46.81 95.43

79.73  2.29

0.874

MCI vs. CN 82.23 80.35

81.54  5.24

0.905

AD vs. MCI vs. CN 72.75 80.90

72.75  2.82

0.887

MMSE

(30)

AD vs. CN 95.85 97.82

96.92  1.62

0.998

AD vs. MCI 69.43 92.19

84.75  3.75

0.914

MCI vs CN 78.34 75.98

77.48  3.43

0.857

AD vs. MCI vs. CN 70.21 80.28

70.21  5.89

0.873

FAQ

(10)

AD vs. CN 90.16 99.56

95.26  1.52

0.982

AD vs. MCI 54.40 92.89

80.24  3.50

0.868

MCI vs. CN 71.83 92.14

79.29  2.26

0.845

AD vs. MCI vs. CN 71.32 85.00

71.33  3.74

0.853

Combined-Test

(53)

AD vs. CN 98.04 100.00

99.28  0.59

1.0

AD vs. MCI 81.38 93.35

89.46  3.93

0.964

MCI vs. CN 90.79 89.08

90.16  3.55

0.960

AD vs. MCI vs. CN 82.43 88.62

82.43  3.92

0.946

Table 4: The classification performance of binary and 3-way multilayer perceptron (MLP) networks using data from the

combined three neuropsychological tests. The class weights and probability thresholds were tuned during the training to

obtain a balanced sensitivity and specificity ratio.

Classification Case Probability Threshold Class Weight SEN% SPE% ACC% AUC

AD vs. CN 0.5 1:1.5 99.47 100.00

99.76  0.48

1.0

AD vs. MCI 0.4 1:1.5 91.49 88.75

89.64  3.94

0.965

MCI vs. CN 0.5 1:1.5 92.07 88.65

90.81  2.91

0.964

AD vs. MCI vs. CN 0.5 1.5:1.5:1 84.28 90.36

84.28  3.66

0.954

Table 5: The multilayer perceptron (MLP) cascading classification performance. The classification performance of the tuned

class weights and probability thresholds are shown.

Classification steps Probability Threshold Class Weight SEN% SPE% ACC% AUC

Step1: CN vs. (AD + MCI) 0.5 1:1 93.27 92.14

92.95  2.33

0.973

Step2: AD vs. MCI using the (AD + MCI)

from step 1

0.6 1:1 86.26 91.15

86.26  3.15

0.957

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4 CONCLUSIONS AND FUTURE

WORK

Most previous studies on AD detection using machine

learning techniques have been focusing on utilizing

brain imaging data. With the rich availability and low

cost of standard neuropsychological tests, this paper

investigated the classification performance for

detecting different cognitive groups (AD, MCI, and

CN) using MLP neural networks. Several important

conclusions can be drawn from this study. First, using

a single neuropsychological test to classify AD and

MCI yielded a very poor sensitivity. Second, the

combination of three neuropsychological test data

with MLP networks showed good potential for early

AD detection. The MLP classifiers performed well on

all three binary cases with the combined-test as well

as for the 3-way classification. Finally, the proposed

cascade MLP approach can further improve the

performance of multi-class classification.

The proposed method is not only reliable but also

cost effective, and therefore it can support large-scale

cognitive screening. Our future work will include

identifying individuals with MCI who would be more

likely to develop AD within a defined period of time.

Additionally, we will investigate other artificial

neural networks on diagnostics and prediction of AD.

We also plan to study the combination of brain

imaging and behavioral data with both machine

learning and deep learning techniques that may offer

additional insights into the progression of various

stages of AD.

ACKNOWLEDGEMENTS

Data used in preparation for this article was obtained

from the Alzheimer's Disease Neuroimaging

Initiative (ADNI) database (adni.loni.usc.edu). As

such, the investigators within the ADNI contributed

to the design and implementation of ADNI and/or

provided data but did not participate in analysis or

writing of this report. A complete listing of ADNI

investigators can be found at:

http://adni.loni.usc.edu/wp-content/uploads/how to

apply/ADNI_Acknowledgement_List.pdf.

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