ArticlePublic health nutrition2019
Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.
Article in Public health nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation.Sensors (Basel, Switzerland) · 2024Pooled it
- Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.Nutrients · 2024Pooled it
- Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2022Pooled it
- Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.Food chemistry: X · 2026Review
- Artificial Intelligence in Religious Certification: A Comprehensive Review of Halal Digitalization, Compliance Integrity, and Future Directions.International journal of food science · 2026Review
- Article
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Relative validity of an intelligent ordering system to estimate dietary intake among university students from a medical school in Shanghai, China.The international journal of behavioral nutrition and physical activity · 2024Article
- NutriGreen image dataset: a collection of annotated nutrition, organic, and vegan food products.Frontiers in nutrition · 2024Article
- NutriBase - management system for the integration and interoperability of food- and nutrition-related data and knowledge.Frontiers in nutrition · 2024Article
- SnackTrack-An App-Based Tool to Assess the Influence of Digital and Physical Environments on Snack Choice.Nutrients · 2023Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- Discrepancy between Food Classification Systems: Evaluation of Nutri-Score, NOVA Classification and Chilean Front-of-Package Food Warning Labels.International journal of environmental research and public health · 2022Article
- Applications of knowledge graphs for food science and industry.Patterns (New York, N.Y.) · 2022Review
- Article
- Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system.Frontiers in nutrition · 2022Article
- A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment.Healthcare (Basel, Switzerland) · 2021Review
- Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition.The British journal of nutrition · 2021Review
- Data-Driven Dietary Patterns, Nutrient Intake and Body Weight Status in a Cross-Section of Singaporean Children Aged 6-12 Years.Nutrients · 2021Article
- Food Composition at Present: New Challenges.Nutrients · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis.
designThe methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors.
resultsThe final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %.
conclusionsThe present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.
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