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NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537777/ https://www.ncbi.nlm.nih.gov/pubmed/28653995 http://dx.doi.org/10.3390/nu9070657 |
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author | Mezgec, Simon Koroušić Seljak, Barbara |
author_facet | Mezgec, Simon Koroušić Seljak, Barbara |
author_sort | Mezgec, Simon |
collection | PubMed |
description | Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72%, along with an accuracy of 94.47% on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55%, which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients. |
format | Online Article Text |
id | pubmed-5537777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-55377772017-08-04 NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment Mezgec, Simon Koroušić Seljak, Barbara Nutrients Article Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86.72%, along with an accuracy of 94.47% on a detection dataset containing 130,517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson’s disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55%, which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson’s disease patients. MDPI 2017-06-27 /pmc/articles/PMC5537777/ /pubmed/28653995 http://dx.doi.org/10.3390/nu9070657 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mezgec, Simon Koroušić Seljak, Barbara NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment |
title | NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment |
title_full | NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment |
title_fullStr | NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment |
title_full_unstemmed | NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment |
title_short | NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment |
title_sort | nutrinet: a deep learning food and drink image recognition system for dietary assessment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537777/ https://www.ncbi.nlm.nih.gov/pubmed/28653995 http://dx.doi.org/10.3390/nu9070657 |
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