A Deep Learning Method for Autism Spectrum Disorder

Title

A Deep Learning Method for Autism Spectrum Disorder

Creator

George B.; Chandra Blessie E.; Resmi K.R.

Description

The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Electrical Engineering, Vol-1106, pp. 1-9.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Autism; Deep learning; VGG-16

Coverage

George B., Santhigiri College, Vazhithala, India; Chandra Blessie E., Department of Artificial Intelligence and Machine Learning, Coimbatore Institute of Technology, Coimbatore, India; Resmi K.R., CHRIST(Deemed to be) University, Karnataka, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 18761100; ISBN: 978-981997953-0

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-7954-7_1

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188267619&doi=10.1007%2f978-981-99-7954-7_1&partnerID=40&md5=96c728bdafdfe5f2e1ecc8394a1be059