Benabdallah Fatima Zahra, Drissi El Maliani Ahmed, Lotfi Dounia, El Hassouni Mohammed
Laboratory of Research in Information Technology and Telecommunication (LRIT), Rabat IT Center, Faculty of Sciences, Mohammed V University in Rabat, Rabat B.P. 1014 RP, Morocco.
Laboratory of Research in Information Technology and Telecommunication (LRIT), Rabat IT Center, lFLSH, Mohammed V University in Rabat, Rabat B.P. 1014 RP, Morocco.
J Imaging. 2023 May 31;9(6):110. doi: 10.3390/jimaging9060110.
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%.
自闭症谱系障碍（ASD）是许多研究人员在实现高精度早期诊断方面面临的一个持续障碍。为了推动自闭症检测的发展，对现有基于自闭症的文献中所呈现的研究结果进行确证至关重要。先前的研究提出了自闭症大脑中连接不足和连接过度缺陷的理论。一种基于理论上与上述理论可比的方法的消除方法证明了这些缺陷的存在。因此，在本文中，我们提出了一个框架，该框架通过结合卷积神经网络（CNN）的深度学习增强方法，考虑自闭症大脑中连接不足和连接过度的特性。在这种方法中，创建类似图像的连接矩阵，然后增强与连接改变相关的连接。总体目标是促进对这种疾病的早期诊断。使用来自大型多站点自闭症脑成像数据交换（ABIDE I）数据集 的信息进行测试后，结果表明该方法提供了高达96%的准确预测值。
