Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Domain-Specific Transfer Learning for Gastric Cancer Tissue Classification.

Journal of imaging informatics in medicine·2026
Same author

Retraction Note: Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet.

Scientific reports·2026
Same author

Capsule-enhanced hierarchical vision transformers for rare disease classification from medical images.

Scientific reports·2026
Same author

Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.

Scientific reports·2026
Same author

QRGEC: quantum reinforcement learning with golden jackal optimization for resilient edge cloud coordination in internet computing.

Scientific reports·2026
Same author

QRBT: Quantum Driven Reinforcement Learning for Scalable Blockchain Transaction Processing.

PloS one·2026

Related Experiment Video

Updated: May 27, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.

B N Jagadesh1, Srihari Varma Mantena2, Asha P Sathe3

  • 1School of Computer Science and Engineering, VIT-AP University, Vijayawada, India.

Scientific Reports
|February 15, 2025
PubMed
Summary

This study introduces an advanced computer vision system for accurate food recognition, crucial for nutritional research. The hybrid transformer model achieves 99.83% accuracy, enhancing dietary monitoring and personalized nutrition.

Keywords:
Improved discrete bat algorithmMutually guided image filteringSwin transformerVision transformerVisual geometry group

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

349

Related Experiment Videos

Last Updated: May 27, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

349

Area of Science:

  • Computer Vision and Machine Learning
  • Nutritional Science and Bioinformatics

Background:

  • Accurate food recognition is vital for nutritional research and dietary monitoring.
  • Existing methods often struggle with dataset noise and complex visual features.

Purpose of the Study:

  • To develop a robust and highly accurate continuous food recognition system.
  • To leverage advanced computer vision and hybrid transformer models for improved performance.

Main Methods:

  • Utilized Mutually Guided Image Filtering (MuGIF) for dataset enhancement.
  • Employed Visual Geometry Group (VGG) for feature extraction.
  • Developed a hybrid transformer model (Vision Transformer and Swin Transformer) optimized with the Improved Discrete Bat Algorithm (IDBA).

Main Results:

  • Achieved a superior classification accuracy of 99.83%.
  • Demonstrated significant performance improvement over existing food recognition methods.

Conclusions:

  • Hybrid transformer architectures combined with advanced preprocessing offer enhanced accuracy and efficiency for food recognition.
  • The proposed system has strong potential for practical applications in dietary monitoring and personalized nutrition recommendations.