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Gluten identification from food images using advanced deep learning and transfer learning methods.

Mayura Tapkire1,2, Vanishri Arun3, M S Lavanya3

  • 1Department of Information Science and Engineering, The National Institute of Engineering, Mysore, India.

Journal of Food Science and Technology
|May 19, 2025
PubMed
Summary

This study introduces a novel Convolutional Neural Network (CNN) model for accurate gluten image classification. The developed food recognition tool aids individuals with celiac disease in identifying gluten-containing foods.

Keywords:
Convolutional neural networks(CNN)EfficientNetGluten image classificationTransfer learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Dietary Management

Background:

  • Food image recognition is crucial for dietary management, especially for individuals with dietary restrictions.
  • Accurate identification of gluten-containing foods is essential for celiac disease management.

Purpose of the Study:

  • To develop a novel approach for gluten image classification using Convolutional Neural Network (CNN) architecture.
  • To assist individuals with celiac disease in identifying gluten-containing foods through accurate food image recognition.

Main Methods:

  • Utilized a curated dataset of 20,000 food images from the Food101 dataset, focusing on common recipes.
  • Employed a Convolutional Neural Network (CNN) architecture, specifically leveraging an EfficientNet pretrained model for image classification.
  • Ensured high-quality training and testing through meticulous data labeling.

Main Results:

  • Achieved a training accuracy of 99.02% and a validation accuracy of 98.38% using the EfficientNet pretrained model.
  • The model demonstrated high effectiveness, obtaining an accuracy of 99% on a test set of 2000 images.
  • Results highlight the model's capability in accurate gluten classification.

Conclusions:

  • The developed food image recognition model is effective for gluten classification.
  • This tool offers significant potential utility for celiac patients in managing their dietary intake.
  • The study contributes a valuable resource to the field of food image recognition for dietary management.