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Updated: May 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

Summary

This summary is machine-generated.

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 algorithm Mutually guided image filtering Swin transformer Vision transformer Visual geometry group

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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.