SegNet-Based Deep Representation Learning for Dysphagia Classification
Author:
Affiliation:
1. Indian Institute of Science,Department of Electrical Engineering,Bangalore,India,560 012
2. Health Care Global Enterprises Ltd,Dept. of Head and Neck Surgery,Bangalore,India,560 002
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747732.pdf?arnumber=9747732
Reference23 articles.
1. Dysphagia and its effects on swallowing sounds and vibrations in adults
2. A comparison between swallowing sounds and vibrations in patients with dysphagia
3. Classifying Dysphagic Swallowing Sounds with Support Vector Machines
4. Age-Related Changes to Eating and Swallowing Impact Frailty: Aspiration, Choking Risk, Modified Food Texture and Autonomy of Choice
5. Automatic Classification of Volumes of Water Using Swallow Sounds from Cervical Auscultation
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1. Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations;Scientific Reports;2024-08-31
2. Enhancing Intake Monitoring: Transfer Learning for Audio-Based Detection of Swallowing Events;2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON);2024-06-25
3. An Automated Classification and Segmentation of Fatty Liver Disease Using SegNet Model on Ultrasound Images;2023 International Conference on Emerging Research in Computational Science (ICERCS);2023-12-07
4. Mealcoach: Contact Microphone-Based Meal Supervision For Post-Stroke Dysphagia Patients;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24
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