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Academic Journal of Computing & Information Science, 2023, 6(11); doi: 10.25236/AJCIS.2023.061106.

A Review of Deep Learning Method for Image Recognition and Nutritional Assessment of Dishes


Chen Xieyu, Tang Na

Corresponding Author:
Chen Xieyu

School of Business, Geely University of China, Chengdu, 641423, China


Image recognition and nutritional assessment of dishes is a research field that has received wide attention in recent years. With the improvement of people's living standards and the demand for a healthy diet, the development of image-based dish recognition and nutritional assessment methods is therefore of great practical importance. This paper reviews the research on dish image recognition and nutritional assessment in terms of recognition types, learning methods, and nutritional assessment models and their application areas. First, this paper introduces different types of dish image recognition, including manual feature-based dish image recognition and deep feature-based image recognition. Second, this paper discusses the commonly used learning methods, including transfer learning and small sample learning. Then, this paper discusses the relationship between dish image recognition and nutritional assessment and introduces some commonly used assessment methods, such as multimodal and pre-trained RNN(Recurrent Neural Network ). Finally, this paper summarizes the application areas of dish image recognition and nutritional assessment methods, including smart catering, health management, and food safety. This paper will help researchers gain a deeper understanding of the latest advances in dish image recognition and nutritional assessment, and provide references for research and applications in related fields.


Image Recognition, Nutritional Assessment, Deep Learning

Cite This Paper

Chen Xieyu, Tang Na. A Review of Deep Learning Method for Image Recognition and Nutritional Assessment of Dishes. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 40-45. https://doi.org/10.25236/AJCIS.2023.061106.


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