An Advanced Approach for Predicting ROP Stages: Deep Learning Algorithms and Belief Function Technique

Authors

  • Nazar Salih National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia / Control and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, Sfax, Tunisia https://orcid.org/0000-0003-1977-9387
  • Mohamed Ksantini Control and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, Sfax, Tunisia https://orcid.org/0000-0002-9928-8643
  • Nebras Hussein Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
  • Donia Ben Halima Control and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, Sfax, Tunisia https://orcid.org/0000-0002-3893-6213
  • Ali Abdul Razzaq Ibn AL Haitham Teaching Eye Hospital, Baghdad, Iraq
  • Sohaib Ahmed Ibn AL Haitham Teaching Eye Hospital, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2024.65.7.39

Keywords:

deep learning, fundus images, ROP, belief function, dempster theory

Abstract

A significant cause of blindness in preterm infants is retinal retinopathy of prematurity (ROP). Early detection and intervention are essential for preventing visual loss. This study proposes an advanced approach for predicting ROP stages using deep learning algorithms and belief function theory. Three steps comprise the suggested technique: image pre-processing, feature extraction using deep learning models, and classification utilizing belief function theory. We used a dataset of 3720 retinal images from premature infants and achieved a classification accuracy of 95.57% for predicting ROP stages. Our results demonstrate the effectiveness of deep learning algorithms and belief function theory in ROP diagnosis. This strategy can increase the efficacy and precision of ROP diagnosis, improving the treatment course for premature infants at risk of vision loss.

 

Author Biographies

Mohamed Ksantini, Control and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, Sfax, Tunisia

 

 

Nebras Hussein, Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq

 

 

Donia Ben Halima, Control and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, Sfax, Tunisia

 

 

Ali Abdul Razzaq, Ibn AL Haitham Teaching Eye Hospital, Baghdad, Iraq

 

 

Sohaib Ahmed, Ibn AL Haitham Teaching Eye Hospital, Baghdad, Iraq

 

 

Downloads

Published

2024-07-30

Issue

Section

Computer Science

How to Cite

An Advanced Approach for Predicting ROP Stages: Deep Learning Algorithms and Belief Function Technique. (2024). Iraqi Journal of Science, 65(7), 4047-4060. https://doi.org/10.24996/ijs.2024.65.7.39

Similar Articles

1-10 of 1295

You may also start an advanced similarity search for this article.