Identification of Autism Spectrum Disorder (ASD) using Autoencoder
Priya Vaijayanthi R1, Ashok Gunturu2, Vamsi Krishna3
1Priya Vaijayanthi R*, Department of CSE, GMR Institute of Technology, Rajam, AP.
2Ashok Gunturu, UG Student, Department of CSE, GMR Institute of Technology, Rajam, AP.
3Vamsi Krishna, Department of CSE, GMR Institute of Technology, Rajam, AP.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 27, 2020. | Manuscript published on February 10, 2020. | PP: 945-948 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1157029420/2020©BEIESP | DOI: 10.35940/ijitee.D1157.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deep Learning (DL) techniques are computational models based on representation learnings. They are demonstrated to be the best reasonable strategies to deal with information with various portrayals and with numerous degrees of reflection. Recognizable proof of ASD has been a test as there is no demonstrated reason for it. The issue has been tended to by numerous specialists with the utilization of fMRI. As MRI and its varieties have 3D representations, Machine Learning and Deep Learning techniques are appropriate to deal with and handle them. This paper extends the recognizable proof of ASD from fMRI pictures utilizing Autoencoder organize. The examinations are led on the benchmark dataset ABIDE II. Results uncover that DL strategies are bringing out better classifiers delivering a great degree of arrangement exactness.
Keywords: ASD, ABIDE, Deep Learning, Machine Learning
Scope of the Article: Learning, Machine