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- W4366308369 abstract "Attention Deficit Hyperactivity Disorder (ADHD) is a common psychiatric disorder among young children. However, there is no accurate and efficient method to diagnose ADHD up to now, due to the complexity of the pathological mechanism and clinical symptoms. This paper aims to present a spatio-temporal method for classification of ADHD and Typical Developing Children (TDC) using Resting-State functional Magnetic (rs-fMRI). To extract the most discriminative features in both space and time dimensions, 3-Dimensional Convolutional Neural Network (3D-CNN) and Gated Recurrent Unit (GRU) were respectively used to process 3D spatial and 1D temporal information in rs-fMRI. Before GRU, 1D filters with different scales were employed to capture significant features of different time intervals from temporal input. To evaluate proposed method, the 5-fold cross validation was employed using ADHD-200 global competition dataset. As a result, the average accuracy, sensitivity, specificity were 71.65 %, 68.00 % and 73.80 %, respectively. Experiment results show that our method performs better than existing methods, and our model not only has good generalization ability, but maintains a balance between sensitivity and specificity. We believe that our method can be used to build a more accurate automatic assistant diagnosis tool of ADHD." @default.
- W4366308369 created "2023-04-20" @default.
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- W4366308369 date "2022-10-01" @default.
- W4366308369 modified "2023-09-24" @default.
- W4366308369 title "Deep Spatio-Temporal Method for ADHD Classification Using Resting-State fMRI" @default.
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- W4366308369 doi "https://doi.org/10.1109/ictai56018.2022.00165" @default.
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