Skip to main content

Dietary Assessment and Nutritional Analysis Using Deep Learning

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 844)

Abstract

Nutrition management is an important feature in day-to-day life from prevention of obesity and chronic diseases related to food intake. To facilitate proper and adequate nutrition intake, suitable dietary assessment is essential. This dietary assessment method based on food recognition has the ability to detect the volume, based on the area of the image taken through computer vision. There has been many modern techniques developed recently on image-based dietary assessment. These techniques have shown trustworthy results on overcoming issues on epidemiology on dietary studies and multiple challenges. This model provides a detailed outline of how features are extracted from the image using computing algorithms, methodology used, and mathematical methods for image recognition. This model provides volume and mass estimation of the given food based on the area occupied by the food. From this, the detailed nutritional analysis was calculated for dietary assessment. The overall accuracy of 94.68% was achieved for classification of food which outperforms the existing methods.

Keywords

  • Volume estimation
  • Food detection
  • Dietary assessment
  • Nutritional analysis

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-16-8862-1_2
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-981-16-8862-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Chang X, Ye H, Albert P, Edward D, Nitin K, Carol B (2013) Image-based food volume estimation. CEA13

    Google Scholar 

  2. He H, Kong F, Tan J (2016) DietCam: multiview food recognition using a multikernel SVM. IEEE J Biomed Health Inform 20(3):848–855

    CrossRef  Google Scholar 

  3. Aguilar E, Remeseiro B, Bolanos M, Radeva P (2020) Grab, Pay and eat: semantic food detection for smart restaurants. IEEE Tans Multimedia 1(1)

    Google Scholar 

  4. Yang S, Chen M, Pomerleau D, Sukthankar R (2010) Food recognition using statistics of pairwise local features. In: IEEE computer society conference on computer vision and pattern recognition, 2010

    Google Scholar 

  5. Bhavya sree B, Yashwanth Bharadwaj V, Neelima N (2021) An inter-comparative survey on state-of-the-art detectors- R-CNN, YOLO and SSD. Smart Innovation, Syst Technol 213:475–483

    Google Scholar 

  6. Madan K, Bhanu Anusha K, Pavan Kalyan P, Neelima N (2019) Research on different classifiers for early detection of lung nodules. Int J Recent Technol Eng 1037–1040

    Google Scholar 

  7. Raju JVVSN, Rakesh P, Neelima N (2019) Driver drowsiness monitoring system. Smart Innovation Syst Technol (SIST) 169:675–683

    Google Scholar 

  8. Eldridge A, Piernas C, Illner A-K, Gibney M, Gurinović M, Vries JD, Cade J (2019) ‘Evaluation of new technology-based tools for dietary intake assessment—An ilsi europe dietary intake and exposure task force evaluation.’ Nutrients 11(1):55

    CrossRef  Google Scholar 

  9. Lo FP-W, Sun Y, Qiu J, Lo B (2018) Food volume estimation based on deep learning view synthesis from a single depth map. Nutrients

    Google Scholar 

  10. Jiang L, Qiu B, Liu X, Huang C, Lin K (2020) DeepFood: food image analysis and dietary assessment via deep model. IEEE Access 8:47477–47489

    CrossRef  Google Scholar 

  11. Subhi MA, Ali SH, Mohammed MA (2019) Vision-based approaches for automatic food recognition and dietary assessment: a survey. IEEE Access 7:35370–35381

    CrossRef  Google Scholar 

  12. Lo FPW, Sun Y, Qiu J, Lo B (2020) Image-based food classification and volume estimation for dietary assessment: a review. IEEE J Biomed Health Inf 24(7):1926–1939

    CrossRef  Google Scholar 

  13. Neelima N, Seenivasa eddy E (2017) An efficient approach to CBIR using DWT and quantized histogram. In: International journal of ınnovative computing, ınformation and control, IJICIC (Scopus-Q1), vol 13(1), pp 157–166

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Madhumitha, S., Magimaa, M., Maniratnam, M., Neelima, N. (2022). Dietary Assessment and Nutritional Analysis Using Deep Learning. In: Bindhu, V., Tavares, J.M.R.S., Du, KL. (eds) Proceedings of Third International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-16-8862-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8862-1_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8861-4

  • Online ISBN: 978-981-16-8862-1

  • eBook Packages: EngineeringEngineering (R0)