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A Deep Learning Approach to Detectin...

Wilhelm, Patrick T.

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A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy./
作者:	Wilhelm, Patrick T.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	52 p.
附註:	Source: Masters Abstracts International, Volume: 82-02.
Contained By:	Masters Abstracts International82-02.
標題:	Electrical engineering. -
電子資源:	https://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:

649834
Electrical engineering.
Subjects--Index Terms:

deep learning

A Deep Learning Approach to Detecting Dysphagia in Videofluoroscopy.
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500 $a Source: Masters Abstracts International, Volume: 82-02.
500 $a Advisor: Reinhardt, Joseph M.
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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.
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