- Title
- Application of Deep Learning in Automated Meal Recognition
- Creator
- Mao, Jiaxiang; Tran, Dat; Ma, Wanli; Naumovski, Nenad; Kellett, Jane; Martinez-Marroquin, Elisa; Slattery, Andrew
- Relation
- 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Proceedings of the Machine Learning and Applications (ICMLA) (Canberra, Australia 13-16 December, 2020) p. 58-63
- Publisher Link
- http://dx.doi.org/10.1109/SSCI47803.2020.9308482
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- Deep learning is a widely used data analysis tool and has its proven value in solving problems and challenges in data science. In the nutrition domain, automated recognition of meals is an essential task within the food quality control and diet management. Adequate food supply and precise distribution of nutrients are extremely important. The availability of deep learning to facilitate these tasks would improve a critical step of the meal service process. Therefore, the aim of this research is to study deep learning applications as automated meal recognition for patients at the Canberra Hospital, specifically using convolutional neural networks (CNN). The application of applying deep learning to food quality control are important in reducing human mistakes that may result to providing wrong foods to patients in the current food service at Canberra Hospital.
- Subject
- food science; food quality control; CNN; deep learning
- Identifier
- http://hdl.handle.net/1959.13/1446405
- Identifier
- uon:42859
- Identifier
- ISBN:9781728125473
- Language
- eng
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