Estimating Nutritional Composition from Food Volume Via Deep Learning-Based Depth and Segmentation Models Article Swipe
Anh Duc Le
,
Anh Do
,
Thanh Nguyen
,
Binh P. Nguyen
,
An V. Tran
,
Nha Tran
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.9734/ajrcos/2025/v18i5650
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.9734/ajrcos/2025/v18i5650
Nutrition plays a critical role in human health, with a balanced diet being essential for preventing non-communicable diseases, enhancing immune function, and improving quality of life. However, dietary imbalances contribute to significant global health issues, including obesity and malnutrition, which have far-reaching economic and health consequences. This research aims to address these challenges by developing a method for estimating the nutritional content of food items from a single 2D image. Our approach integrates a U-Net architecture with a ResNet18 encoder for depth prediction and employs FoodSAM for precise food segmentation. These components enable the calculation of food volume and mass, which are then used to estimate nutritional content based on the USDA database. Experimental results show that our model achieves a mean relative error (MRE) ranging from 11.18% to 50.35% for individual food items. Furthermore, our method maintains consistent mass predictions across various scenarios, including complex food combinations. This method demonstrates robustness in handling foods with diverse shapes and colors, providing a solid foundation for practical dietary tracking applications. By enabling nutritional monitoring, our approach has the potential to support public health initiatives and promote healthier lifestyles.
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Metadata
- Type
- article
- Language
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- Landing Page
- https://doi.org/10.9734/ajrcos/2025/v18i5650
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409680245Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.9734/ajrcos/2025/v18i5650Digital Object Identifier
- Title
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Estimating Nutritional Composition from Food Volume Via Deep Learning-Based Depth and Segmentation ModelsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-22Full publication date if available
- Authors
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Anh Duc Le, Anh Do, Thanh Nguyen, Binh P. Nguyen, An V. Tran, Nha TranList of authors in order
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https://doi.org/10.9734/ajrcos/2025/v18i5650Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.9734/ajrcos/2025/v18i5650Direct OA link when available
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Volume (thermodynamics), Deep learning, Segmentation, Composition (language), Artificial intelligence, Food composition data, Computer science, Food science, Biology, Art, Orange (colour), Physics, Quantum mechanics, LiteratureTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.value | 0.17838652 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |