Matching Items (385)
ContributorsWard, Geoffrey Harris (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-18
ContributorsBolari, John (Performer) / ASU Library. Music Library (Publisher)
Created2018-10-04
ContributorsOftedahl, Paul (Performer) / ASU Library. Music Library (Publisher)
Created2018-09-29
ContributorsMarshall, Kimberly (Performer) / Meszler, Alexander (Performer) / Yatso, Toby (Narrator) / ASU Library. Music Library (Publisher)
Created2018-09-16
ContributorsTaylor, Karen Stephens (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-21
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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

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.

ContributorsCarreto, Cesar (Author) / Pizziconi, Vincent (Thesis director) / Vernon, Brent (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
ContributorsCramer, Craig (Performer) / ASU Library. Music Library (Publisher)
Created1997-02-16
ContributorsMarshall, Kimberly (Performer) / ASU Library. Music Library (Publisher)
Created2019-03-17
ContributorsCampbell, Jeffrey (Performer) / ASU Library. Music Library (Publisher)
Created2005-10-23