The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Outstanding Reviewers

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2024.01506154
PDF

Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models

Author 1: Mailen Gonzalez
Author 2: Jose M. Fuertes Garcia
Author 3: Manuel J. Lucena Lopez
Author 4: Ruben Abdala
Author 5: Jose M. Massa

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 6, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The assessment of bone trabecular quality degrada-tion is important for the detection of diseases such as osteoporosis. The gold standard for its diagnosis is the Dual Energy X-ray Absorptiometry (DXA) image modality. The analysis of these images is a topic of growing interest, especially with artificial intelligence techniques. This work proposes the detection of a degraded bone structure from DXA images using some approaches based on the learning of Trabecular Bone Score (TBS) ranges. The proposed models are supported by intelligent systems based on convolutional neural networks using two kinds of approaches: ad hoc architectures and knowledge transfer systems in deep network architectures, such as AlexNet, ResNet, VGG, SqueezeNet, and DenseNet retrained with DXA images. For both approaches, experimental studies were made comparing the proposed models in terms of effectiveness and training time, achieving an F1-Score result of approximately 0.75 to classify the bone structure as degraded or normal according to its TBS range.

Keywords: Osteoporosis; Dual Energy X-ray Absorptiometry (DXA); Trabecular Bone Score (TBS); Classification; Convolutional Neural Network (CNN)

Mailen Gonzalez, Jose M. Fuertes Garcia, Manuel J. Lucena Lopez, Ruben Abdala and Jose M. Massa. “Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01506154

@article{Gonzalez2024,
title = {Bone Quality Classification of Dual Energy X-ray Absorptiometry Images Using Convolutional Neural Network Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01506154},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01506154},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Mailen Gonzalez and Jose M. Fuertes Garcia and Manuel J. Lucena Lopez and Ruben Abdala and Jose M. Massa}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.