Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning.
J Acoust Soc Am
; 145(5): EL423, 2019 05.
Article
in En
| MEDLINE
| ID: mdl-31153323
ABSTRACT
The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Speech
/
Tongue
/
Deep Learning
/
Movement
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Acoust Soc Am
Year:
2019
Type:
Article
Affiliation country:
United States