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A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study [article]

Ahmed El-Gazzar, Mirjam Quaak, Leonardo Cerliani, Peter Bloem, Guido van Wingen, Rajat Mani Thomas
<span title="2020-02-14">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We evaluate our proposed model on the publicly available ABIDE dataset to demonstrate the capability of our model to classify Autism Spectrum Disorder (ASD) from resting-state fMRI data.  ...  Thus, fMRI scans are represented as 4-Dimensional (3-space + 1-time) tensors. And it is widely believed that the spatio-temporal patterns in fMRI manifests as behaviour and clinical symptoms.  ...  Method In this work we present a novel architecture for 4D rs-fMRI data with application to ASD classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.05981v1">arXiv:2002.05981v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lwpqucodyfbizpeiwcfi7cfzkm">fatcat:lwpqucodyfbizpeiwcfi7cfzkm</a> </span>
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