An Efficient 1DCNN–LSTM Deep Learning Model for Assessment and Classification of fMRI-Based Autism Spectrum Disorder-Reference-Cited by-同舟云学术

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An Efficient 1DCNN–LSTM Deep Learning Model for Assessment and Classification of fMRI-Based Autism Spectrum Disorder

Published:2022 Issue: Volume: Page:1039-1048
ISSN:2367-4512
Container-title:Innovative Data Communication Technologies and Application
language:
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Author:

Qayyum Abdul,Ahamed Khan M. K. A.,Benzinou Abdesslam,Mazher Moona,Ramasamy Manickam,Aramugam Kalaiselvi,Deisy C.,Sridevi S.,Suresh M.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-7167-8_77

Reference20 articles.

1. M.P. van den Heuvel, H.E. Hulshoff Pol, Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519–534 (2010). https://doi.org/10.1016/j.euroneuro.2010.03.008

2. A. Riaz, M. Asad, E. Alonso, G. Slabaugh, Fusion of fMRI and non-imaging data for ADHD classification. Comput. Med. Imaging Graph. 65, 115–128 (2018). https://doi.org/10.1016/j.compmedimag.2017.10.002

3. K. Rajpoot, A. Riaz, W. Majeed, N. Rajpoot, Functional connectivity alterations in epilepsy from resting-state functional MRI. PLoS ONE 10(8), e0134944 (2015). https://doi.org/10.1371/journal.pone.0134944

4. A. Qayyum, I. Ahmad, W. Mumtaz, M.O. Alassafi, R. Alghamdi, M. Mazher, Automatic segmentation using a hybrid dense network integrated with an 3D-atrous spatial pyramid pooling module for computed tomography (CT) imaging. IEEE Access 8, 169794–169803 (2020). https://doi.org/10.1109/ACCESS.2020.3024277

5. A. Qayyum, I. Razzak, W. Mumtaz, Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals (2020), pp. 245–257

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