An Effective Sparse Autoencoders based Deep Learning Framework

for fMRI Scans Classification

Abeer M. Mahmoud

1,2 a

, Hanen Karamti

2,3 b

and Fadwa Alrowais

Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman

University, PO Box 84428, Riyadh, Saudi Arabia

MIRACL Laboratory, ISIMS, University of Sfax, B.P. 242, 3021 Sakiet Ezzit, Sfax, Tunisia

Keywords: Sparse Autoencoder, Autism, Medical Image Classification.

Abstract: Deep Learning (DL) identifies features of medical scans automatically in a way very near to expert doctors

and sometimes over beats in treatment procedures. In fact, it increases model generalization as it doesn’t

focus on low level features and reduces difficulties (eg: overfitting) of training high dimensional data.

Therefore, DL becomes a prioritized choice in building most recent Computer-Aided Diagnosis (CAD)

systems. From other prospective, Autism Spectrum Disorder (ASD) is a brain disorder characterized by

social miscommunication and confusing repetitive behaviours. The accurate diagnosis of ASD through

analysing brain scans of patients is considered a research challenge. Some appreciated efforts has been

reported in literature, however the problem still needs enhancement and examination of different models. A

multi-phase learning algorithm combining supervised and unsupervised approaches is proposed in this paper

to classify brain scans of individuals as ASD or controlled patients (TC). First, unsupervised learning is

adopted using two sparse autoencoders for feature extraction and refinement of optimal network weights

using back-propagation error minimization. Then, third autoencoder act as a supervised classifier. The

Autism Brain fMRI (ABIDE-I) dataset is used for evaluation and cross-validation is performed. The

proposed model recorded effective and promising results compared to literatures.

https://orcid.org/0000-0002-0362-0059

https://orcid.org/0000-0001-5162-2692

https://orcid.org/0000-0002-8447-198X

1 INTRODUCTION

Deep Learning is an artificial intelligence approach

that offers automatic learning features similar to

experts in many fields but especially in computer

vision and imaging domains. The potential satisfying

feedback of applying deep learning methods in

medical imaging encouraged many researchers to

prioritize the approach while solving their research

challenges and faced problems (Krizhevsky et al.,

2012, Najafabadi et al., 2015;Litjens et al., 2017,

Ravì D., et al., 2017). Many neural network models

of varies number of layers to transform input images

to outputs through accurate extraction of most

discriminative features were proposed. However, the

convolutional neural network (CNN) (Bengio et al.,

2013) is highly a recommended selection. CNN

contains layers that transform the input with

convolution filters of a small extend. It is preferred

as it doesn’t waste time in learning separate

detectors for identical objects placed differently in

an image, and it reduce the number of network

training parameters as weight have no direct relation

with image size.

For many years, autism disorder has received

more attention as an important disease. It is a

significant crisis for many families in Arabic society

because unknown reasons for causing and poor

background of the characteristic of the disease.

Autism appears since birth and it recorded a variant

and multiple symptoms of illness. Many researches

tried to diagnose the disease from different data

types using machine learning techniques (SE.

Schipul et al., 2012; M. Plitt et al,2015; A. Abraham,

540

Mahmoud, A., Karamti, H. and Alrowais, F.

An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classiﬁcation.

DOI: 10.5220/0009397605400547

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 540-547

ISBN: 978-989-758-423-7

et al.,2017; ). ABIDE-I is a large autism brain scans

images dataset. Knowledge discovery in this datasets

is a challenge for many researchers. Therefore

several machine learning approaches (G. Chanel, et

al., 2016; XA. Bi,2018; G. Chanel, et al.,2019 ) and

deep learning (Xi. Li, et al., 2018; H. Li, et al., 2018)

have been reported on ABIDE dataset. The data

contains 1035 individuals between autistic (ASD)

and controlled patients (TCs). These models may

suffer from lack of generalization of low reported

classification accuracy. Also, some researchers used

the full set of data while others used it partially due

to availability of resources and/or characteristics of

the used technique. However, the domain still opens

and encourages competition with different novel

hybridization methods and/or enrichment of

knowledge extraction or mining results from

medical data such ABIDE.

In this paper, an attempt to build a generalized

model for dealing with large medical imaging data is

proposed. It is a new Deep Learning framework

based on sparse autoencoder where it allows

machines to learn very complex data representation

that can subsequently be used to perform accurate

data classification in a fully treatment for ABIDE

datasets. Two conventional sparse autoencoders

(SAE) are unsupervised learning used in training to

extract discriminative features. The third one is a

supervised learning used to classify the extracted

features and to diagnose the ASD. These three stages

were jointed to form a stacked framework. The rest

of the paper is as follow: Section 2 presents related

work on ABIDE. Section 3 shows our contributions

framework construction, features extraction and

images classification. Section4 discusses computa-

tional results and section 5 concludes the paper.

2 RELATED WORK

Autism is neurological developmental disorder that

the disorder ,affect many families and recently

spread due to many unclarifed reasons. More

attention focuses on rehabilitation of families to

handle their cases however an intensive research

currently focusing on the treatment and/or

knowledge discovery in autism medical imaging.

fMRI scans of brain are a kind of medical imaging.

Each fMRI scan is actually a group of tiny cubic

elements called voxels(X,Y, Z or 3D) and if time is

added during data gathering , it become 4D. A time

series is extracted from each voxel to save its

activity change over time. One of the famous brain

scans for autism is ABIDE. In the following, some

related work on ABIDE with different techniques.

(H. Chen, et al., 2016), investigated the effect of

different frequency bands for constructing brain

functional network, and obtained 79.17% accuracy

using SVM technique applied to 112 ASD and 128

healthy control subjects. (Brown et al., 2018),

proposed framework based on an element-wise layer

for deep neural networks. Then they incorporate the

data-driven structural priors. They select 1013 of

539 healthy control and 474 with ASD and reported

68.7%. (XA. Bi, et al., 2018), selected the support

vector machines (SVM) as a classifier but used

multiply SVM architecture to enhance the results as

single SVM gives poor results. Their selected

samples included 46 TC and 61 ASD and recorded

96.15%. (Bi Xia-an, et al, 2018), proposed genetic-

evolutionary SVM and validated by data of 157

participants (86 AS and 71 TC). The classification

accuracy reached to 97.5%. (XA. Bi, et al, 2018),

presented multiple Random Neural network (NNs)

based model on ABIDE. They focused on 50 ASD

and 42 TCs samples. A random 5 NN clusters were

built using 5 different NNs. The highest accuracy

cluster is selected as the best base classifier. Then,

valuable features were used to retrieve abnormal

brain regions.

In addition, several deep learning models have

been proposed recently. (Xi. Li, et al, 2018), used

3D CNN to detect features based on the spatial

characteristics, then they voted the results through

visualization and interpretation to choose the most

competent for ASD or TCs output. They

implemented their proposed on subset of 82

diagnosed with autism child and 48 controlled. They

obtained higher accuracy. (X. Guo, et al., 2017),

proposed a DNN with a novel method for extraction

of features for high dimensional rs-fMRI. They used

multiple trained sparse auto-encoders for feature

extraction, and then used DNN for high-quality

representations of the whole-brain function

connectivity patterns. They considered 110 samples

(55 ASD and 55 TCs) and recorded 86.36%. (M.

Khosla, et al., 2018), proposed 3D deep learning

model and used subset of data. They classified

healthy controld individuals by 76 67% classifcation

accuracy of using subset of 178 samples for training.

However generalization of proposed models is

affected by small samples.

A two phase’s method was proposed in (Xi. Li,

et al., 2018). First, a deep neural network classifier

was trained with original scans. Then a corruption

on the regions of interest (ROIs) of the brain scans

An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classiﬁcation

541

were feed to the trained network to enhance

perdition. Their approach was tested on 82 subset

and reached 85.3% accuracy. (Heinsfeld et al.,

2018), proposed the usage of respective neural

patterns of functional connectivity in rs-fMRI as a

main discriminative features guide in classifying

healthy versus autism patients. They implemented

unsupervised phase, where they used 2 stacked

denoising autoencoders. Then they used multi-layer

perceptron as a supervised classifier. They applied

the proposed method on the whole data set and

reported 70%, it is considered an improvement with

almost full data.

(H. Li,, et al., 2018), proposed a deep transfer

learning NN framework. They trained a stacked

sparse autoencoder offline extract the functional

connectivity patterns. They selected a subset of 310

samples, and reported a range between 62.3% and

70.4%. (Eslami, T., et al., 2019), proposed a new

joint learning procedure combining autoencoder and

a single layer perceptron. Then continued with

results of first phase and applied a data

augmentation strategy, based on linear interpolation

to produce synthetic training datasets. They used

only 13 sites from 17 and reported 80%.

The literature showed that a few studies have

considered sparse autoencoder in the classification

of individuals with autism based on fMRI. Also,

only one reference implemented their proposed

system on the dataset fully. Hence, the motivation of

our proposed method where a full treatment of

ABIDE database is evaluated.

3 PROPOSED SPARSE AES

BASED DL FRAMEWORK

3.1 Sparse Autoencoder

Autoencoder consists of basically three-layers, input,

hidden, and output, respectively. The hidden layer is

fully connected to the input and output layers

through weighted connections. It is trained to

reconstruct similar input at output layer effectively.

One of autoencoders disadvantages is the limited

number of hidden units. The Spar Autoencoder

(SAE) (Makhzani & Frey., 2013; fgJanowczyk, A..,

et al., 2017; Hou, L., et al., 2019) tried to solve this

by adding a sparsity constraint, to tune a large

number of neurons with low average output and

hence neurons appear schematic inactive most of the

time. This can be implemented by setting a loss

function during training. Assume hidden neurons

activation function = haj, then average activation of

it is in Eq.1

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∑

h







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(1)

The objective of sparsity constraints is to minimize

so that A

=A , where A is a sparsity constraint

between 0 such as 0.05 , A ̂

, the average activation

of hidden unit j (in the sparse autoencoder), p

is the

penalty term and N= number of neurons in the

hidden layers



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∑

KLA||A



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



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(2)

Figure 1: Proposed Sparse autoencoder based deep learning model for medical data classification.

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542

Figure 2: Un-supervised sparse autoencoders for feature extraction.

To enforce sparsity constraints regularizes

complexity and prevent over-fitting, a penalty term

is added to cost function which penalizes A ̂, de-

weighting significantly from A. Since A and A ̂, can

be seen as the probabilities of Bernoulli random

variables, a Kullback-Leibler (KL) divergence (Shin

et al.,2013), represented by Eq.3.

KLA|A





A log

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

1A



log



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(3)

3.2 Proposed Architecture &

Extracting Features

The proposed architecture is shown in Figure 1. It is

composed of two Sparse AEs as unsupervised

learning for feature extraction and followed by AE

for classification task:

 Input layer: receiving the pre-processed fMRI

scans.

 Two Sparse AEs: are two unsupervised

autoencoders; for deeper feature extraction and

refinement of highly important set of unique

features. Figure 2, shows the unsupervised role

of the two SAE to reconstruct input while

learning most valuable feature.

 Output layer: is a supervised autoencoder to

classify cases into ASD or TC.

The unsupervised Sparse AEs receives data without

labels. Each Sparse autoencoder contains two

convolutional (CNN) layers. Normalization and

Max-pooling are necessary for smoothing the

learning process. Then the identical set of

parameters are kept for decoding phase (eg: kernel

size (3,3,3) for dimensionality reduction). To reach

the nearest reconstruction of its input at the output

layer, the AE is forced to infer major information

preserving a reduction while representing the input

in the hidden layer, then mapped to the output layer.

Therefore, each hidden node represents a feature of a

reduced but accurate copy and this can be evaluated

through visualization, See figure 2. SAE has a

sparsity constraint that is imposed on the mean

activity of hidden layer (Shin et al.,2013) to

overcome overfitting.

The input and output layers for the first

autoencoder have 17668 features fully connected to

hidden bottleneck of 3015 units from the hidden

layer. The second autoencoder maps 3015 inputs

from the output of the previous autoencoder to

outputs through a hidden layer of 983 units and then

to 271 units. The batch size=256 and epoch=80. To

classify the individuals with ASD, we used

supervised autoencoder, inserted in the last layer

(output layer) of the proposed neural network. The

discriminative features were injected to last

autoencoder, in addition to the correspondence

vector of numbers for each class of output (one-hot).

The vector consists of only 1 for the class it

represents and all others are zeros. Softmax function

is used for regression of output. Batch size=1024

and epoch=100, are used.

3.3 Training, Validation and Testing

First, each raw rs-fMRI data was preprocessed, and

the whole-brain function connectivity patterns (FCP)

were obtained by calculating the Pearson’s

Correlation coefficient (CC) of Time series (TSs)

from any pair of ROIs. Given two times series, T

and T

, each of length T

, the Pearson’s correlation

can be computed Eq.4

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∑



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(4)

An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classiﬁcation

543

Multiple SAEs were used in training phase to

conclude low but valuable and most discriminative

representations of data. In fact single SAE usually

gives less expectation, however two SAEs deeper

the learning and enrich result. These in turn will

affect the classifier positively. Training, testing,

optimizing parameters, and 10-fold cross validation

flow of the proposed model, is shown in Figure 3.

First, the original fMRI is consisting of a number of

slices. Applying region segmentation on each slice,

then calculating functional connectivity analysis.

The normalization of input is necessary for

autoencoder in general.

Figure 3: The proposed Deep framework for training, validation, and testing.

Figure 4: Sample of focused and excluded slices during training.

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Table 1: ABIDE-I providers class distribution of genders, ASD and TC.

Site

Caltech

CMU

KKI

Leuven

MaxMun

NYU

OHSU

OLIN

PITT

SBL

SDSU

STANFORD

TRINITY

UCLA

USM

YALE

Ts 37 27 48 63 52 175 26 34 56 30 36 39 47 98 140 71 56

ASD 19 14 20 29 24 75 12 19 29 15 14 19 22 54 66 46 28

Male 15 11 16 25 21 65 12 15 25 15 13 15 22 48 57 46 20

Female 4 3 4 3 3 10 0 3 4 0 1 4 0 6 9 0 8

TC 18 13 28 34 28 100 14 15 27 15 22 20 25 44 74 25 28

Male 14 9 20 29 27 73 14 13 23 15 16 16 25 38 57 25 20

Female 4 4 8 5 1 27 0 2 4 0 6 4 0 6 17 0 8

Accuracy versus number of epoch (1-100) (b)

classification loss and validation loss versus number

of epoch (1-100).

After training and validation are accomplished, an

optimized set of parameters are saved for later

testing. The data were divided 70% training and

30% testing.

The performance of the system was evaluated

based on the four criteria: Sensitivity (SE),

Speciﬁcity (SP), Accuracy (ACC) and Matthew’s

Correlation Coefﬁcient (MCC). These are calculated

based on the accurate identiﬁcation of positive

(ASD) or negative (TC) samples. A True Positive

(TP) would indicate that autism fMRI labeled and

identiﬁed correctly through data description, while a

False Positive (FP) indicates that fMRI is identiﬁed

as normal individual. Conversely, True Negatives

and False Negatives (FN) are calculated for

controlled individual. The metrics are deﬁned by the

following equations:

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

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

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(5)

𝑀

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

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

(6)

Where N=T

, S=(T

)/N and

P=(T

)/N.

4 DATA ACQUISITION &

EXPERMINTES

The Autism data ABIDE I used in this paper was

acquired public through request for Autism Brain

Imaging Data Exchange to use it in research purpose

only. Table 1, shows the class and gender from 17

sites participation of (ABIDE I), (ADi. Martino, et

al., 2014) Pre-processed Connectomes Project

(http://preprocessed-connectomesproject.org/). C-

PAC pipeline was chosen for pre-processed version

(Y. Behzadi, et al., 2007). Actually, the project

offers four pipelines to download data all provide

basic requirement of handling data but they are

different in the pre-processing algorithm. C-PAC

was chosen to compare our results with other

research paper that used same pipeline data.

Figure 5: (a) Classification accuracy and validation, (b)

loss accuracy and validation.

For more details about pre-processing the raw data

please read (ADi. Martino, et al., 2014). 1035

samples were obtained after excluding the corrupted

samples, 505 ASD and 530 TC. Technical

Implementation All aspects of data pre-processing,

analysis, feature extraction and building the

classifier are implemented using python and

colaboratory resources. The used lab top is with intel

An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classiﬁcation

545

(R) core (TM) i5, 7200 U processor 2.5 GHz and 8

GB of RAM specification.

The data 3D volume is 61×73 ×61. First, during

visualization, the centred slices provides clearer and

consistent information while the beginning 10 slices

and last as well seems to be a burden while training,

validating and testing and provide no valuable

information from our prospective. Therefore, the

centred 40 slice were chosen as in figure 4.

Additionally, the data was smoothed and masked,

and ROI are calculated from time difference in slices

as seen in figure 1. Sparse autoencoder like the basic

autoencoder needs normalization (scaling issues and

vectors flatten) for data to be suitable for neural

network. The model was trained over 70% of the

data and 30% for testing and was used in a cross-

validation 10-fold schema.

The main performance measure is the accuracy

of correctly classify ASD from TC patients.

Therefore the proposed network training accuracy

and validation accuracy, training loss and validation

loss, versus epochs is depicted in Figure 5. The

mean values are higher with increase of epoch

number. The results are reasonable as more training

epoch increase the classification and validation

accuracy and reduce the loss as in Figure 5(b).

Figure 6: Results comparison of Techniques in (A.

Heinsfeld, et al., 2018) and SAE-DL.

Also, a comparison of accuracy, sensitivity and

specificity with author that applied Support Vector

Machine (SVM, Acc=0.65, Se=0.68, Sp=0.62),

Random Forest (RF, Acc=0.63, Se=0.69, Sp=0.58)

and Deep Neural Network (DNN, Acc=0.70,

Se=0.74, Sp=0.63), on the full data set (A.

Heinsfeld, 2018). Figure 6, shows these values in

comparison with our method in two cases. Case1:

selecting 40 centred slices (SAE-DL-40, Acc=0.76,

Se=0.78, Sp=0.67), and Case2: using full slices

(SAE-DL-full slices(73), Acc=0.65, Se=0.67,

Sp=0.57),). Based on mentioned literatures, and the

depicted similar case of using full database sample,

the proposed classification accuracy in case1,

reported higher classification accuracy due to

removing the burden of useless slices of patients that

provide poor information and reduces the learning

capabilities.

5 CONCLUSIONS

In the present paper, a two sparse autoencoders deep

learning framework was developed for classifying

ASD individuals and TD controls based on fMRI

brain scans. The first contribution of this work is the

proposed architecture for feature selection based on

multiple sparse AEs to improve the quality of the

extracted features and treats issues like over fitting

and generalization. The second contribution is using

full data sets with accompany complexities, where

many researchers avoid full set and preferred a

partial sub set of ABIDE. The proposed framework

trained and evaluated by the 10-fold evaluation was

implemented. An accuracy of 76% was achieved

with fMRI data, thus achieving higher predicative

performance than the literatures of techniques

applied on the same data.

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