An optimal hybrid AI-ResNet for accurate severity detection and classification of patients with aphasia disorder-Reference-Cited by-同舟云学术

An optimal hybrid AI-ResNet for accurate severity detection and classification of patients with aphasia disorder

Published:2023-08-11 Issue:8 Volume:17 Page:3913-3922
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Nivedha E.,Chandrasekar A.,Jothi S.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-023-02620-0.pdf

Reference29 articles.

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3. Grönberg, A., Henriksson, I., Lindgren, A.: Accuracy of NIH stroke scale for diagnosing aphasia. Acta Neurol. Scand. 143(4), 375–382 (2021)

4. Barbera, D.S., Huckvale, M., Flemin, V., Upton, E., Coley-Fisher, H., Doogan, C., Shaw, I., Latham, W., Leff, A.P., Crinion, J.: NUVA: a naming utterance verifier for aphasia treatment. Comput. Speech Lang. 69, 101221 (2021)

5. Kristinsson, S., Zhang, W., Rorden, C., Newman-Norlund, R., Basilakos, A., Bonilha, L., Yourganov, G., Xiao, F., Hillis, A., Fridriksson, J.: Machine learning-based multimodal prediction of language outcomes in chronic aphasia. Hum. Brain Mapp. 42(6), 1682–1698 (2021)

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