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Updated: Jul 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning

Koon Meng Ang¹, Wei Hong Lim¹, Sew Sun Tiang¹

¹Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia.

Biomimetics (Basel, Switzerland)

|November 24, 2023

Summary

This summary is machine-generated.

This study presents ETLBOCBL-CNN, an automated method for optimizing convolutional neural network (CNN) architectures. It achieves high classification accuracy on diverse image datasets, advancing smart device infrastructure.

Keywords:

automatic network design deep learning architecture hyperparameter optimization image classification teaching–learning-based optimization

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Convolutional Neural Networks (CNNs) are crucial for image classification.
Optimizing CNN architectures is complex and computationally intensive.
Existing methods often struggle with diverse and complex classification tasks.

Purpose of the Study:

To introduce ETLBOCBL-CNN, an automated approach for optimizing CNN architectures.
To enhance the discovery of novel and effective CNN structures.
To improve classification accuracy across various image datasets.

Main Methods:

ETLBOCBL-CNN utilizes an encoding scheme for network and learning hyperparameters.
It incorporates competency-based learning and stochastic peer interaction.
A tri-criterion selection scheme optimizes fitness, diversity, and improvement rates.

Main Results:

ETLBOCBL-CNN achieved high accuracies on nine image datasets, including MNIST (99.72%) and Rectangles (99.99%).
The method demonstrated superior performance compared to state-of-the-art techniques.
Significant improvements were noted across datasets with varying complexities.

Conclusions:

ETLBOCBL-CNN effectively automates CNN architecture optimization.
The approach shows strong potential for enhancing smart device infrastructure development.
Its competency-based learning and selection schemes drive innovation in CNN design.