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

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

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
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • 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
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130956
PDF

Identification of Retinal Disease using Anchor-Free Modified Faster Region

Author 1: Arulselvam. T
Author 2: S. J. Sathish Aaron Joseph

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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

Abstract: Infections of the retinal tissue, as well as delayed or untreated therapy, may result in visual loss. Furthermore, when a large dataset is involved, the diagnosis is prone to inaccuracies. As a consequence, a completely automated model of retinal illness diagnosis is presented to eliminate human input while maintaining high accuracy classification findings. ODALAs (Optimal Deep Assimilation Learning Algorithms) are unable to handle zero errors or covariance or linearity and normalcy. DLTs (Deep Learning Techniques) such as GANs (Generative Adversarial Networks) or CNNs might replace the numerical solution of dynamic systems (Convolution Neural Networks), in order to speed up the runs. With this objective, this study proposes a completely automated multi-class retina disorders prediction system in which pictures from the Fundus image dataset are upgraded using RSWHEs (Recursive Separated Weighted Histogram Equalizations) to boost contrast and noise is eliminated using the Wiener filter. The improved picture is used for segmentation, which is done using clustering and the optimum threshold. The suggested EFFCM is used for clustering (Enriched Fast Fuzzy C Means). The suggested AOO (Adaptive optimum Otsu) threshold technique is used for clustering and picture optimal thresholding. This work suggests AMF-RCNNs (anchor-free modified faster region-based CNNs) that integrate AFRPNs (anchor free regions proposal generation networks) with Improved Fast R-CNNs into single networks for detecting retinal issues accurately. The performances of Accuracy is 98.5%, F-Measure is 96.5%, Precession is 99.2% and different Subset features are 98.5 % show better results when compared with other related techniques or models.

Keywords: Retinal disease; fundus image dataset; contrast enhancement; segmentation; Fast Fuzzy C Means; adaptive optimal OTSU; faster region-based convolutional neural network

Arulselvam. T and S. J. Sathish Aaron Joseph, “Identification of Retinal Disease using Anchor-Free Modified Faster Region” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130956

@article{T2022,
title = {Identification of Retinal Disease using Anchor-Free Modified Faster Region},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130956},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130956},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Arulselvam. T and S. J. Sathish Aaron Joseph}
}



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

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 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

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

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

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org