Open AccessIdentification of Autism Spectrum Disorder (ASD) using Autoencoder
Author(s) -
Priya Vaijayanthi R*,
Ashok Gunturu,
Vamsi Krishna
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1157.029420
Subject(s) - autoencoder , benchmark (surveying) , autism spectrum disorder , artificial intelligence , representation (politics) , computer science , identification (biology) , machine learning , deep learning , reflection (computer programming) , autism , pattern recognition (psychology) , psychology , developmental psychology , biology , botany , geodesy , politics , political science , law , programming language , geography
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.