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A Deep Learning Approach to Detectin...

The University of Iowa.

A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy./
Author:	Wilhelm, Patrick T.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	52 p.
Notes:	Source: Masters Abstracts International, Volume: 82-02.
Contained By:	Masters Abstracts International82-02.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27963852
ISBN:	9798662480827

A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy.
Wilhelm, Patrick T.

A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 52 p.

Source: Masters Abstracts International, Volume: 82-02.

Thesis (M.S.)--The University of Iowa, 2020.

This item must not be sold to any third party vendors.

Dysphagia, or a disorder classified by difficulty swallowing, is a severe health problem that reduces the quality of life of those affected. The standard method to diagnose dysphagia is the X-ray video fluoroscopic swallowing exam (VFSE). In this paper, we investigate the use of deep learning networks to classify VFSE as normal or abnormal. We have based our network on a long term recurrent convolutional network (LRCN). 1154 VFSE were available to train the network. Using 10-fold cross-validation, the accuracy of classification was 85%, and the area under the ROC curve was 0.89. This work shows the promise of using deep learning networks as a screening tool to detect dysphagia in VFSE.

ISBN: 9798662480827Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

deep learning

A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy.
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020 $a 9798662480827
035 $a (MiAaPQ)AAI27963852
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100 1 $a Wilhelm, Patrick T. $3 1335292
245 1 0 $a A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 52 p.
500 $a Source: Masters Abstracts International, Volume: 82-02.
500 $a Advisor: Reinhardt, Joseph M.
502 $a Thesis (M.S.)--The University of Iowa, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Dysphagia, or a disorder classified by difficulty swallowing, is a severe health problem that reduces the quality of life of those affected. The standard method to diagnose dysphagia is the X-ray video fluoroscopic swallowing exam (VFSE). In this paper, we investigate the use of deep learning networks to classify VFSE as normal or abnormal. We have based our network on a long term recurrent convolutional network (LRCN). 1154 VFSE were available to train the network. Using 10-fold cross-validation, the accuracy of classification was 85%, and the area under the ROC curve was 0.89. This work shows the promise of using deep learning networks as a screening tool to detect dysphagia in VFSE.
590 $a School code: 0096.
650 4 $a Electrical engineering. $3 596380
650 4 $a Artificial intelligence. $3 559380
650 4 $a Medical imaging. $3 1180167
653 $a deep learning
653 $a dysphagia
653 $a videofluoroscopy
690 $a 0544
690 $a 0800
690 $a 0574
710 2 $a The University of Iowa. $b Electrical and Computer Engineering. $3 1179482
773 0 $t Masters Abstracts International $g 82-02.
790 $a 0096
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27963852

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