Cargando…
A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous wor...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299127/ https://www.ncbi.nlm.nih.gov/pubmed/37367458 http://dx.doi.org/10.3390/jimaging9060110 |
_version_ | 1785064287064031232 |
---|---|
author | Benabdallah, Fatima Zahra Drissi El Maliani, Ahmed Lotfi, Dounia El Hassouni, Mohammed |
author_facet | Benabdallah, Fatima Zahra Drissi El Maliani, Ahmed Lotfi, Dounia El Hassouni, Mohammed |
author_sort | Benabdallah, Fatima Zahra |
collection | PubMed |
description | Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%. |
format | Online Article Text |
id | pubmed-10299127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102991272023-06-28 A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection Benabdallah, Fatima Zahra Drissi El Maliani, Ahmed Lotfi, Dounia El Hassouni, Mohammed J Imaging Article Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%. MDPI 2023-05-31 /pmc/articles/PMC10299127/ /pubmed/37367458 http://dx.doi.org/10.3390/jimaging9060110 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Benabdallah, Fatima Zahra Drissi El Maliani, Ahmed Lotfi, Dounia El Hassouni, Mohammed A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection |
title | A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection |
title_full | A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection |
title_fullStr | A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection |
title_full_unstemmed | A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection |
title_short | A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection |
title_sort | convolutional neural network-based connectivity enhancement approach for autism spectrum disorder detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299127/ https://www.ncbi.nlm.nih.gov/pubmed/37367458 http://dx.doi.org/10.3390/jimaging9060110 |
work_keys_str_mv | AT benabdallahfatimazahra aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT drissielmalianiahmed aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT lotfidounia aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT elhassounimohammed aconvolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT benabdallahfatimazahra convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT drissielmalianiahmed convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT lotfidounia convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection AT elhassounimohammed convolutionalneuralnetworkbasedconnectivityenhancementapproachforautismspectrumdisorderdetection |