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.
Details
Title
- Deep Learning Application to Improve Quality of Life in Diabetes
Contributors
- Carreto, Cesar (Author)
- Pizziconi, Vincent (Thesis director)
- Vernon, Brent (Committee member)
- Harrington Bioengineering Program (Contributor)
- Barrett, The Honors College (Contributor)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2021-05
Resource Type
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