Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning

Jonghye Woo, Fangxu Xing, Jerry L. Prince, Maureen Stone, Jordan R. Green, Tessa Goldsmith, Timothy G. Reese, Van J. Wedeen, Georges El Fakhri

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)EL423-EL429
JournalJournal of the Acoustical Society of America
Volume145
Issue number5
DOIs
StatePublished - May 1 2019

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Acoustics and Ultrasonics

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