Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment.

中文翻译：

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基于 DWT-CNN 的强大 CAD 系统，使用基于任务的 fMRI 进行自闭症早期诊断


基于任务的功能磁共振成像 (TfMRI) 是一种诊断成像模式，用于观察疾病或其他状况对大脑功能活动的影响。自闭症谱系障碍 (ASD) 是一种与社交和语言能力损伤相关的普遍性发育障碍。机器学习算法已广泛应用于旨在客观 ASD 诊断的脑成像。最近，深度学习方法在功能磁共振成像分类方面受到越来越多的关注。本文的目标是开发一个基于卷积神经网络 (CNN) 的框架，以帮助使用从语音实验响应中收集的 TfMRI 数据对 ASD 进行全局诊断。