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Related Experiment Video

Updated: May 20, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Improved food recognition using a refined ResNet50 architecture with improved fully connected layers.

Pouya Bohlol1, Soleiman Hosseinpour1, Mahmoud Soltani Firouz1

  • 1Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

Current Research in Food Science
|March 24, 2025
PubMed
Summary

This study developed a deep learning system using ResNet50 to accurately identify food items from videos, aiding in health impact analysis in dining settings. The optimized model achieved 97.25% accuracy in food recognition.

Keywords:
Deep learningIranian foodOptimized hyperparamtersRefined ResNet50Specific connected layers

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Food Science

Background:

  • Food consumption significantly impacts human health, influenced by factors like quality, quantity, freshness, and color.
  • Accurate identification of consumed food is crucial for evaluating health impacts, especially in controlled environments like hospitals and restaurants.

Purpose of the Study:

  • To develop and evaluate a machine vision system utilizing deep learning for food identification across 16 categories.
  • To assess the impact of food consumption on human health by analyzing meal data from hospital and restaurant settings.

Main Methods:

  • A dataset of 12,000 food images was created and augmented to 66,000 images.
  • Five deep learning algorithms were employed, with ResNet50 showing superior performance.
  • Hyperparameter tuning and transfer learning were used to optimize the ResNet50 model, including a customized fully connected layer.

Main Results:

  • The optimized ResNet50 model achieved 97.25% accuracy and 0.2 loss in food recognition.
  • The best-performing model utilized the Adam optimizer, an initial learning rate of 10⁻³, batch size of 4, and image size of 340x640.
  • Training and response times were 5.30 hours and 1.2 seconds, respectively.

Conclusions:

  • ResNet50, particularly with a customized fully connected layer, is highly effective for accurate and efficient food recognition.
  • This technology can support health impact assessments by providing precise data on food consumption in various settings.