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2000
Volume 18, Issue 8
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Aim

Deep learning models, such as deep convolutional neural networks (CNNs), have undergone extensive scrutiny in the context of food classification because of their exceptional feature extraction capabilities.

Background

Similarly, ensemble-based learning approaches have exhibited great potential for achieving effective supervised classification.

Objective

We suggest an innovative approach to improve the effectiveness of deep learning-based food classification.

Methods

Our proposal involves a novel deep learning ensemble framework that draws inspiration from the fusion of deep learning models with ensemble learning based on random subspaces. The random subspaces play a role in diversifying the ensemble system in a straightforward but impactful way. Moreover, to enhance the classification accuracy even more, we explore transfer learning, employing the migration of acquired weights from a single classifier to another (namely, CNNs). This approach expedites the process of learning.

Results

Results from experiments conducted using well-established food datasets illustrate that the suggested deep learning ensemble system delivers competitive performance compared to state-of-the-art techniques, as evidenced by its classification accuracy.

Conclusion

The amalgamation of deep learning and ensemble learning holds substantial promise for dependable food categorization.

© 2025 Bentham Science Publishers
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2024-09-19
2025-09-28

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Deep Ensemble Learning using Transfer Learning for Food Classification
Recent Advances in Computer Science and Communications 18, E26662558310306 (2025); https://doi.org/10.2174/0126662558310306240911071501
/content/journals/rascs/10.2174/0126662558310306240911071501
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  • Article Type:     Research Article
Keyword(s): clustering; convolutional neural network (CNN); deep learning; Random subspace; support vector machine; velocity profiles

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