Conference proceeding
A Deep Learning Approach to Video Fluoroscopic Swallowing Exam Classification
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1647-1650
04/2020
DOI: 10.1109/ISBI45749.2020.9098510
Abstract
Dysphagia, or difficulty swallowing, is a serious 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. The proposed network is based on a long term recurrent convolutional network (LRCN). This network was trained and validated using 1154 VFSE. 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.
Details
- Title: Subtitle
- A Deep Learning Approach to Video Fluoroscopic Swallowing Exam Classification
- Creators
- Patrick Wilhelm - University of IowaJoseph M Reinhardt - University of IowaDouglas Van Daele - Roy J. and Lucille A. Carver College of Medicine
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1647-1650
- Publisher
- IEEE
- DOI
- 10.1109/ISBI45749.2020.9098510
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Radiology; Radiation Oncology; Medicine Administration; Otolaryngology; Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984197120702771
Metrics
17 Record Views