Semiannual Publication
Document Type : Original Article
Abstract
In recent decades, autism spectrum disorder (ASD) has displayed an incremental prevalence rate. Due to the unavailability of a definite cure, the early diagnosis of the disorder is of high significance. There is evidence suggesting the discriminable differences between the resting state networks of people who suffer from the disorder and healthy individuals. This distinguishability allows for the utilization of fMRI imaging to perform as a good instrument for the identification of autism spectrum disorder. In this paper, a tensor decomposition method for the diagnosis of autism from fMRI images is presented. The selected dataset for testing the performance of the proposed algorithm is ABIDE1. All sites of the ABIDE1 are used for training the algorithm which is a challenging problem in fMRI data analysis. Our proposed method successfully achieves the classification performance of about 60% for all site analyses.
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