Full metadata
Title
Deep Learning Application to Improve Quality of Life in Diabetes
Description
Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.
Date Created
2021-05
Contributors
- Carreto, Cesar (Author)
- Pizziconi, Vincent (Thesis director)
- Vernon, Brent (Committee member)
- Harrington Bioengineering Program (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
21 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2020-2021
Handle
https://hdl.handle.net/2286/R.I.64067
Level of coding
minimal
Cataloging Standards
System Created
- 2021-05-01 12:23:01
System Modified
- 2021-08-11 04:09:57
- 3 years 3 months ago
Additional Formats