A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection

被引:4
|
作者
Benabdallah, Fatima Zahra [1 ]
El Maliani, Ahmed Drissi [1 ]
Lotfi, Dounia [1 ]
El Hassouni, Mohammed [2 ]
机构
[1] Mohammed V Univ Rabat, Fac Sci, Rabat IT Ctr, Lab Res Informat Technol & Telecommun LRIT, BP 1014 RP, Rabat, Morocco
[2] Mohammed V Univ Rabat, Rabat IT Ctr, Lab Res Informat Technol & Telecommun LRIT, lFLSH, BP 1014 RP, Rabat, Morocco
关键词
autism; deep learning; Rs-fMRI; connectivity; BRAIN;
D O I
10.3390/jimaging9060110
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
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%.
引用
收藏
页数:12
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