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Deep Neural Networks for Image-Based Dietary Assessment
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Deep neural network for food image classification and nutrient identification: A systematic review.

Rajdeep Kaur1, Rakesh Kumar1, Meenu Gupta2

  • 1Department of Computer Science & Engineering, Chandigarh University, Punjab, India.

Reviews in Endocrine & Metabolic Disorders
|March 28, 2023
PubMed
Summary

This study reviews how food image classification using machine learning can help track nutrient intake. It explores technology

Keywords:
CNNDLFICFine tuningNutrientsPre-trained modelsTL

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Area of Science:

  • Computer Science
  • Nutrition Science
  • Health Informatics

Background:

  • Technology, particularly social media and mobile devices, offers benefits like improved communication but also poses health risks such as sleep disturbances and obesity.
  • Tracking food intake is crucial for understanding health impacts, with technology offering potential solutions.
  • A systematic review is necessary to analyze existing research on technology-assisted food intake tracking.

Purpose of the Study:

  • To systematically review and analyze research on food image classification (FIC) and nutrient estimation using machine learning.
  • To identify current techniques, datasets, performance metrics, and challenges in the field of FIC.
  • To present a case study demonstrating the application of FIC and object detection for nutritional analysis.

Main Methods:

  • A systematic literature review was conducted following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Guidelines.
  • Searches were performed on major scientific databases (Web of Science, Scopus, IEEE Xplore) using keywords related to food image analysis and machine learning.
  • Extracted articles were screened, and 56 relevant studies were selected for detailed analysis based on specific criteria.

Main Results:

  • The review identified various investigations employing different FIC techniques and nutrient estimation solutions.
  • Key aspects analyzed include food image datasets, hyperparameter tuning, algorithms used, performance evaluation, and identified challenges.
  • A case study was developed to illustrate the practical application of FIC and object detection for estimating nutritional content from food images.

Conclusions:

  • Food image classification and machine learning offer a promising avenue for objective nutrient estimation and dietary assessment.
  • Further research is needed to address challenges related to dataset diversity, model generalizability, and real-world implementation.
  • Technology-driven approaches like FIC can contribute to better health management by providing accurate insights into food consumption.