Abstract
Over the past few years, a significant part of scientific research has been focused on the assistance of patients who suffer from obesity or diabetes. Monitoring the food intake through self-report in diet control applications has been proven both time-consuming and non-practical and can be easily sidelined especially by children. In this paper, we propose the design and development of a novel system, which will assist obese or diabetic patients. We have implemented transfer learning as well as fine-tuning to different pre-trained CNN models to automatically distinguish dessert from non-dessert food images. For further training of these deep neural networks, a new dataset was constructed, which derived from the original Food-101 dataset. To be precise, 19 categories of desserts were used, which correspond to 19K images combined with 19K images of non-desserts. Google InceptionV3 architecture appeared to have the best performance, reaching a validation accuracy of 95.89%. To demonstrate feasibility of out platform and the independence of data biases, we constructed another data collection of food images, which was captured under challenging light and angle of capture conditions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shields, M., Tremblay, M.S., Connor Gerber, S., Janssen, I.: Abdominal obesity and cardiovascular disease risk factors within body mass index categories. Heal. Rep. 23(2), 7–15 (2012)
Vucetic, I., Stains, J.P.: Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann. N. Y. Acad. Sci. 1271(1), 37–43 (2012). https://doi.org/10.1111/j.1749-6632.2012.06750.x
Tate, E.B., et al.: mHealth approaches to child obesity prevention: successes, unique challenges, and next directions. Transl. Behav. Med. 3(4), 406–415 (2013). https://doi.org/10.1007/s13142-013-0222-3
Smith, A.J., Skow, A., Bodurtha, J., Kinra, S.: Health information technology in screening and treatment of child obesity: a systematic review. Pediatrics 131(3), e894–e902 (2013). https://doi.org/10.1542/peds.2012-2011
Lau, P.W.C., Lau, E.Y., Wong, D.P., Ransdell, L.: A systematic review of information and communication technology–based interventions for promoting physical activity behavior change in children and adolescents. J. Med. Internet Res. 13(3), e1533 (2011). https://doi.org/10.2196/jmir.1533
Abril, E.P.: Tracking myself: assessing the contribution of mobile technologies for self-trackers of weight, diet, or exercise. J. Health Commun. 21(6), 638–646 (2016). https://doi.org/10.1080/10810730.2016.1153756
O’Mahony, N., et al.: Deep learning vs. Traditional computer vision. In: Arai, K., Kapoor, S. (eds.) Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1, pp. 128–144. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-17795-9_10
Amato, G., Bolettieri, P., de Lira, V.M., Muntean, C.I., Perego, R., Renso, C.: Social media image recognition for food trend analysis. In: SIGIR 2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017)
Bossard, L., Guillaumin, M., Van Gool, L.: Food 101 - mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision - ECCV 2014. ECCV 2014 Lecture Notes in Computer Science, vol 8694. Springer, Cham, pp 446–461 (2014). https://doi.org/10.1007/978-3-319-10599-4_29
Şengür, A. Akbulut, Y. Budakm, Ü.: Food image classification with deep features. In: 2019 International Artificial Intelligence and Data Processing Symposium (IDAP)
Attokaren, D., Fernandes, I., Sriram, A., Murthy, Y.V., Koolagudi, S.: Food classification from images using convolutional neural networks. In: Proceeding of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, 5–8 Nov 2017
Alexiadis, A., Triantafyllidis, A., Elmas, D., Gerovasilis, G., Votis, K., Tzovaras, D.: A social robot-based platform towards automated diet tracking. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 11–14 (2020)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, pp. 2818–2826 (2016) https://doi.org/10.1109/CVPR.2016.308
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. IEEE Conf. CVPR 2016, 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Published as a Conference Paper at ICLR (2015)
Howard, A.G.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). https://arxiv.org/abs/1704.04861
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. Proc. Track. 9, 249–256 (2010)
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement «GATEKEEPER/857223 Smart Living Homes – Whole Interventions Demonstrator for People at Health and Social Risks» (KOH.021064).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Exarchou, DM., Alexiadis, A., Triantafyllidis, A., Ioannidis, D., Votis, K., Tzovaras, D. (2022). A Deep Learning-Based Dessert Recognition System for Automated Dietary Assessment. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-06368-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06367-1
Online ISBN: 978-3-031-06368-8
eBook Packages: Computer ScienceComputer Science (R0)