Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks

Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks

Background: Video fluoroscopic swallowing study (VFSS) is considered as the gold standard diagnostic tool for evaluating dysphagia. However, it is time consuming and labor intensive for the clinician to manually search the recorded long video image frame by frame to identify the instantaneous swallo...

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Bibliographic Details
Main Authors: Ki-Sun Lee, Eunyoung Lee, Bareun Choi, Sung-Bom Pyun
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Diagnostics
Subjects:
videofluoroscopic swallowing study
action recognition
deep learning
convolutional neural network
transfer learning
Online Access:https://www.mdpi.com/2075-4418/11/2/300

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https://www.mdpi.com/2075-4418/11/2/300

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