Imperial College London
Portrait Picture

DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Reader
 
 
 
//

Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
//

Location

 

Bessemer BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Lo:2020:10.1109/JBHI.2020.2987943,
author = {Lo, FPW and Sun, Y and Qiu, J and Lo, B},
doi = {10.1109/JBHI.2020.2987943},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1926--1939},
title = {Image-based food classification and volume estimation for dietary assessment: a review.},
url = {http://dx.doi.org/10.1109/JBHI.2020.2987943},
volume = {24},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.
AU  - Lo,FPW
AU  - Sun,Y
AU  - Qiu,J
AU  - Lo,B
DO  - 10.1109/JBHI.2020.2987943
EP  - 1939
PY  - 2020///
SN  - 2168-2194
SP  - 1926
TI  - Image-based food classification and volume estimation for dietary assessment: a review.
T2  - IEEE Journal of Biomedical and Health Informatics
UR  - http://dx.doi.org/10.1109/JBHI.2020.2987943
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32365038
UR  - https://ieeexplore.ieee.org/document/9082900
UR  - http://hdl.handle.net/10044/1/80916
VL  - 24
ER  -
Download