Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network - Sherkatghanad, Zeinab; Akhondzadeh, Mohammadsadegh; Salari, Soorena; Zomorodi-Moghadam, Mariam; Abdar, Moloud; Acharya, U. Rajendra; Khosrowabadi, Reza; Salari, Vahid (Frontiers in Neuroscience, 2020)
Background: Convolutional Neural Networks (CNN) have provided a signiﬁcant achievement in different machine learning tasks such as speech recognition, image classiﬁcation, automotive software engineering, together with some substantial applications in neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method In this paper, we focused on the diagnosis of the autism spectrum disorder (ASD) via CNN using a large brain imaging dataset. We classiﬁed ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data represented by a multi-site database known as Autism Brain Imaging Data Exchange (ABIDE). The proposed approach was able to classify individuals with autism compared to typical controls based on the patterns of functional connectivity. The outcome measure is accuracy, sensitivity, and speciﬁcity of the prediction of ASD from control subjects. Results: The experimental results indicate that our proposed model with 70.22 % diagnostic accuracy in classiﬁcation of the ASD outperforms the previous works on ABIDE I dataset and for the CC400 functional parcellation atlas of the brain. Also, it was shown that the number of parameters used in our CNN model is fewer than the best known study in the ASD classiﬁcation which leads to the reduction of the training time. The existing best-known method had a huge number of parameters, 19,961,200, in theirs ﬁnal stage wheras we reduced it to 4,398,80221 parameters. The sensitivity and speciﬁcity were also measured in this study as part of our report