Research Article

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Volume: 4 Number: 1 April 30, 2021
EN

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Abstract

Brain injuries are significant disorders affecting human life. Some of these damages can be completely eliminated by methods such as drug therapy. On the other hand, there is no known permanent treatment for damages caused by diseases such as Alzheimer, Autism spectrum disorder (ASD), Multiple sclerosis and Parkinson. Treatments aimed at slowing the progression of the disease are generally applied in these types of disorders. For this reason, essential to diagnose the disease at an early phase before behavioral disorders occur. In this study, a study is presented to detect ASD through resting-state functional magnetic resonance imaging rs-fMRI. However, fMRI data are highly complex data. Within the study's scope, ASD and healthy individuals were distinguished on 871 samples obtained from the ABIDE I data set. The long short-term memory network (LSTM), convolutional neural network (CNN) , and hybrid models are used together for the classification process. The results obtained are promising for the detection of ASD on fMRI.

Keywords

Supporting Institution

Bandirma Onyedi Eylül University

Project Number

BAP-21-1003-003

Thanks

This study has supported by the Scientific Research Project (BAP) Coordinatorship of Bandirma Onyedi Eylül University under grant number BAP-21-1003-003.

References

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  7. S. J. Blumberg, M. D. Bramlett, M. D. Kogan, L. A. Schieve, J. R. Jones and M. C. Lu, “Changes in prevalence of parent-reported autism spectrum disorder in school-aged US children: 2007 to 2011-2012,” National Center for Health Statistics, no. 65, pp. 1-11, 2013.
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Details

Primary Language

English

Subjects

Artificial Intelligence , Software Engineering (Other)

Journal Section

Research Article

Publication Date

April 30, 2021

Submission Date

February 13, 2021

Acceptance Date

March 23, 2021

Published in Issue

Year 1970 Volume: 4 Number: 1

APA
Bayram, M. A., Özer, İ., & Temurtaş, F. (2021). Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images. Sakarya University Journal of Computer and Information Sciences, 4(1), 142-155. https://doi.org/10.35377/saucis.04.01.879735
AMA
1.Bayram MA, Özer İ, Temurtaş F. Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images. SAUCIS. 2021;4(1):142-155. doi:10.35377/saucis.04.01.879735
Chicago
Bayram, Muhammed Ali, İlyas Özer, and Feyzullah Temurtaş. 2021. “Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on FMRI Images”. Sakarya University Journal of Computer and Information Sciences 4 (1): 142-55. https://doi.org/10.35377/saucis.04.01.879735.
EndNote
Bayram MA, Özer İ, Temurtaş F (April 1, 2021) Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images. Sakarya University Journal of Computer and Information Sciences 4 1 142–155.
IEEE
[1]M. A. Bayram, İ. Özer, and F. Temurtaş, “Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images”, SAUCIS, vol. 4, no. 1, pp. 142–155, Apr. 2021, doi: 10.35377/saucis.04.01.879735.
ISNAD
Bayram, Muhammed Ali - Özer, İlyas - Temurtaş, Feyzullah. “Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on FMRI Images”. Sakarya University Journal of Computer and Information Sciences 4/1 (April 1, 2021): 142-155. https://doi.org/10.35377/saucis.04.01.879735.
JAMA
1.Bayram MA, Özer İ, Temurtaş F. Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images. SAUCIS. 2021;4:142–155.
MLA
Bayram, Muhammed Ali, et al. “Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on FMRI Images”. Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, Apr. 2021, pp. 142-55, doi:10.35377/saucis.04.01.879735.
Vancouver
1.Muhammed Ali Bayram, İlyas Özer, Feyzullah Temurtaş. Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images. SAUCIS. 2021 Apr. 1;4(1):142-55. doi:10.35377/saucis.04.01.879735

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