Apr 21, 2020 · Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD from spatio-temporal fMRI images.

Our results indicate that 4D deep learning models could be beneficial for other learning problems with 4D fMRI data.

Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D ...

This work proposes a 4D spatio-temporal deep learning approach for ASD classification where 4D convolutional neural networks and Convolutional-recurrent ...

Apr 21, 2020 · Overall, we propose 4D deep learning models for ASD classification from 4D fMRI sequences. We demonstrate that a 4D spatio-temporal convGRU- ...

In this study, we developed a deep learning framework named spatial–temporal Transformer (ST-Transformer) to distinguish ASD subjects from typical controls ...

The proposed model is evaluated on the publicly available ABIDE dataset to demonstrate the capability of the model to classify Autism Spectrum Disorder ...

A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: An ABIDE Autism Classification Study. Authors: Ahmed El-Gazzar.

Oct 7, 2019 · We introduce an end-to-end algorithm capable of extracting spatiotemporal features from the full 4-D data using 3-D CNNs and 3-D Convolutional LSTMs.

Aug 24, 2023 · This work presents a novel data-driven deep learning method using fMRI data for ASD identification, which could provide valuable reference for clinical ...