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Updated: Jul 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Multispectral Food Classification and Caloric Estimation Using Convolutional Neural Networks.

Ki-Seung Lee1

  • 1Department of Electrical and Electronic Engineering, Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 05029, Republic of Korea.

Foods (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a multi-spectral imaging technique with convolutional neural networks (CNNs) for automatic food recognition and calorie estimation, aiding in obesity prevention. The method significantly improves accuracy in identifying food types and their caloric content.

Area of Science:

  • Computer Vision
  • Food Science
  • Biomedical Engineering

Background:

  • Accurate food logging is crucial for managing metabolic diseases and obesity.
  • Current methods often require significant user intervention, limiting adherence and accuracy.
  • Automated systems for food type and caloric content analysis are needed.

Purpose of the Study:

  • To develop an automated system for recognizing food type and estimating caloric content using multi-spectral imaging.
  • To enhance the accuracy of food analysis by fusing RGB images with images from ultraviolet (UV), visible, and near-infrared (NIR) spectral regions.
  • To optimize wavelength combinations for improved classification and caloric estimation performance.

Main Methods:

  • Utilized multi-spectral images across UV, visible, and NIR regions (385-1020 nm) combined with RGB images.
Keywords:
convolutional neural networkdata fusiondietary assessmentfood analysismultispectral imagingnon-invasive analysis

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  • Employed a convolutional neural network (CNN) for food item classification and caloric content estimation.
  • Trained the CNN on 10,909 images of 101 food types and validated on 3,636 images.
  • Applied a piecewise selection method to determine optimal wavelength combinations.

    • Main Results:

    • Food classification accuracy increased from 88.9% to 97.1% with the addition of NIR images to RGB.
    • Mean Absolute Percentage Error (MAPE) for caloric estimation decreased from 41.97% to 18.97%.
    • Achieved a highest food type classification accuracy of 99.81% (using 19 images) and lowest MAPE of 10.56% (using 14 images).

    Conclusions:

    • Multi-spectral imaging, particularly incorporating UV and NIR bands, significantly enhances automated food classification and caloric estimation.
    • The developed CNN-based system offers a promising, low-intervention approach for dietary monitoring.
    • This technology has potential applications in preventing obesity and metabolic diseases through improved dietary tracking.