Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review

被引:21
作者
Dalakleidi, Kalliopi, V [1 ]
Papadelli, Marina [1 ]
Kapolos, Ioannis [1 ]
Papadimitriou, Konstantinos [2 ]
机构
[1] Univ Peloponnese, Dept Food Sci & Technol, Kalamata, Greece
[2] Agr Univ Athens, Dept Food Sci & Human Nutr, Lab Food Qual & Hyg, Iera Odos 75 Str, Athens, Greece
关键词
nutrition monitoring; food image recognition; dietary assessment; machine learning; deep learning; artificial intelligence; computer vision; image-based food recognition; NEURAL-NETWORK CLASSIFIER; BRAIN STRUCTURES; SEGMENTATION; MODEL;
D O I
10.1093/advances/nmac078
中图分类号
R15 [营养卫生、食品卫生]; TS201 [基础科学];
学科分类号
100403 ;
摘要
Statement of Significance: The latest advances of computer vision approaches for dietary assessment are described in this review, and recent applications of image-based food recognition systems (IBFRS) in professional dietetic practice are presented. Open issues that should be tackled in the near future via interdisciplinary research to optimize the performance of IBFRS as well as to increase their adoption by the professionals of the field have been examined and discussed. Dietary assessment can be crucial for the overall well-being of humans and, at least in some instances, for the prevention and management of chronic, life-threatening diseases. Recall and manual record-keeping methods for food-intake monitoring are available, but often inaccurate when applied for a long period of time. On the other hand, automatic record-keeping approaches that adopt mobile cameras and computer vision methods seem to simplify the process and can improve current human-centric diet-monitoring methods. Here we present an extended critical literature overview of image-based food-recognition systems (IBFRS) combining a camera of the user's mobile device with computer vision methods and publicly available food datasets (PAFDs). In brief, such systems consist of several phases, such as the segmentation of the food items on the plate, the classification of the food items in a specific food category, and the estimation phase of volume, calories, or nutrients of each food item. A total of 159 studies were screened in this systematic review of IBFRS. A detailed overview of the methods adopted in each of the 78 included studies of this systematic review of IBFRS is provided along with their performance on PAFDs. Studies that included IBFRS without presenting their performance in at least 1 of the above-mentioned phases were excluded. Among the included studies, 45 (58%) studies adopted deep learning methods and especially convolutional neural networks (CNNs) in at least 1 phase of the IBFRS with input PAFDs. Among the implemented techniques, CNNs outperform all other approaches on the PAFDs with a large volume of data, since the richness of these datasets provides adequate training resources for such algorithms. We also present evidence for the benefits of application of IBFRS in professional dietetic practice. Furthermore, challenges related to the IBFRS presented here are also thoroughly discussed along with future directions.
引用
收藏
页码:2590 / 2619
页数:30
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