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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Transforming tabular data into images for deep learning models.

Abdullah Elen1, Emre Avuçlu2

  • 1Department of Software Engineering, Faculty of Engineering and Naturel Sciences, Bandirma Onyedi Eylul University, Bandirma, Balikesir, Turkiye.

Neural Networks : the Official Journal of the International Neural Network Society
|February 15, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a new method to convert numerical tabular data into images, allowing deep learning models to analyze it effectively. This image-based deep learning approach significantly improves classification accuracy on diverse datasets.

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Science

Background:

  • Deep learning (DL) excels with unstructured data (images, text) but struggles with tabular numerical data due to its lack of spatial structure.
  • Traditional machine learning methods often fall short on complex numerical datasets.
  • Bridging the gap between DL and numerical data analysis is crucial for broader AI applications.

Purpose of the Study:

  • To develop a novel method for transforming numerical tabular data into grayscale image representations.
  • To enable the application of deep learning architectures, such as Convolutional Neural Networks (CNNs), to numerical datasets.
  • To enhance the performance and applicability of deep learning in analyzing structured numerical information.

Main Methods:

  • Numerical features are normalized and organized into square image matrices.
Keywords:
Deep learningImage classificationMachine learningNumerical data transformationTabular dataset

Related Experiment Videos

Last Updated: May 6, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
  • Labeled grayscale images are generated from the transformed tabular data.
  • Experiments utilize Residual Network (ResNet-18) and Directed Acyclic Graph Neural Network (DAG-Net) models with 5-fold cross-validation on four public datasets (RMSCD, Optdigits, TUNADROMD, Spambase).

    • Main Results:

    • The Directed Acyclic Graph Neural Network (DAG-Net) achieved high accuracies: 99.91% (RMSCD), 99.77% (Optdigits), 98.84% (TUNADROMD), and 93.06% (Spambase).
    • Ablation studies and efficiency analyses confirmed improvements in training performance and reduced computational costs.
    • The image-based transformation proved effective for classification tasks on numerical datasets.

    Conclusions:

    • The proposed image transformation method offers a practical and efficient strategy for integrating numerical datasets into deep learning workflows.
    • This approach broadens the applicability of deep learning techniques across diverse domains previously challenging for DL.
    • The open-source release of the implementation facilitates reproducibility and encourages further research in this area.