THE INDEPENDENT RESEARCH REGISTRY FOR FOOD, NUTRITION AND HEALTH

 


Nutrition, Data Science and Artificial Intelligence

The Nutrition, Data Science, and Artificial Intelligence special collection aims to explore the intersection of nutrition science, data analytics, and AI. We invite submissions that leverage advanced computational techniques to analyse large-scale dietary data, predict nutritional outcomes, and develop innovative solutions for personalised dietary recommendations and dietary pattern analysis.
 

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  • Study Status: Published
  • Study Type: Review
  • Study Location: Global

Study Title
Deep Neural Network for Food Image Classification and Nutrient Identification: A Systematic Review

Principal Investigator
Rajdeep Kaur, Rakesh Kumar, Meenu Gupta

Affiliation
Department of Computer Science & Engineering, Chandigarh University, Punjab, India

Start Date
Not specified

End Date
Not specified

Study Objective
To evaluate and synthesize research on deep learning (DL) and convolutional neural network (CNN) based techniques for food image classification (FIC) and nutrient identification.

Short Abstract
This systematic review investigates the use of deep neural networks (DNN) for food image classification and nutrient identification. A total of 56 studies were considered after screening 771 articles from major databases. It highlights the challenges in image recognition, dataset availability, and model performance metrics, proposing solutions using transfer learning (TL) and pre-trained models for improving nutrient estimation through image analysis.

Study Design
Systematic review following PRISMA guidelines

Population
Global studies on food image classification and nutrient estimation

Sample Size
771 articles screened, 56 studies included

Inclusion Criteria
Studies using machine learning and deep learning methods for food image classification and nutrient estimation

Exclusion Criteria
Studies not focusing on food image analysis or machine learning-based nutrient identification

Intervention/Exposure
Food image analysis using CNN, DL, and transfer learning (TL)

Outcome Measures
Accuracy of food image classification, nutrient estimation, and model performance metrics

Funding Source
Not specified

Collaborating Institutions
Chandigarh University, Punjab, India

Ethics Approval
Not specified

Publication Status
Published in Rev Endocr Metab Disord (2023).

Keywords
Deep learning, CNN, food image classification, nutrient estimation, transfer learning

Data Collection Methods
Systematic review and article extraction based on performance metrics, hyperparameter tuning, and food image datasets

Primary Data Availability
Not applicable

Contact Information
Meenu Gupta: This email address is being protected from spambots. You need JavaScript enabled to view it.