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Nutrients 2017, 9(7), 657; doi:10.3390/nu9070657

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment

1
Information and Communication Technologies, Jožef Stefan International Postgraduate School, Jamova Cesta 39, 1000 Ljubljana, Slovenia
2
Computer Systems Department, Jožef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Received: 10 March 2017 / Revised: 5 June 2017 / Accepted: 20 June 2017 / Published: 27 June 2017
(This article belongs to the Special Issue Technology Based Approaches to Dietary Intake Assessment)
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Abstract

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. View Full-Text
Keywords: NutriNet; deep convolutional neural networks; deep learning; food recognition; food detection; drink recognition; drink detection; Parkinson’s disease NutriNet; deep convolutional neural networks; deep learning; food recognition; food detection; drink recognition; drink detection; Parkinson’s disease
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mezgec, S.; Koroušić Seljak, B. NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment. Nutrients 2017, 9, 657.

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