• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 10, Issue 1
  3. Author

Online ISSN: 2515-8260

Volume10, Issue1

RULE MINING BASED CLINICAL TRIAL USING TRANSFER LEARNING APPROACH WITH THE GRADING OF DIABETIC RETINOPATHY IN HOME-CENTRIC ENVIRONMENT

    Dr.C.Saravanabhavan,MA.Swedhaa,Dr.P.Preethi,Mrs.K.M.Swarna Devi .

European Journal of Molecular & Clinical Medicine, 2023, Volume 10, Issue 1, Pages 1764-1778

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

The features from the fundus pictures' optic disc must be precisely located before landmark features included in the fundus images can be identified. According to the severity of the DR, existing research employed a variety of Artificial Intelligence (AI) strategies for screening and diagnosing DR sooner to protect diabetic patients from going blind. In contrast to the real-world optimization strategy, the current models, while effective, had limitations related to time consumption and premature convergence. As a result, the Multi-class Transfer Learning that has been proposed is updated to improve performance based on its fundamental design. The model was trained to find solutions more effectively thanks to the higher convergence. The suggested approach gets around constraint problems and improves accuracy with better feature selection. In comparison to the current transfer model, which achieved 98.94% accuracy for DIARETDB1, the suggested method achieved an accuracy of 97.46%. The e-ophtha dataset, however, achieved accuracy of 98.91%. The predicted algorithm can be integrated with any device at home-centric environment.
Keywords:
    Retino e-ophtha transfer learning ResNet Diabetic Retinopathy and ImageNet
  • PDF (697 K)
  • XML
(2023). RULE MINING BASED CLINICAL TRIAL USING TRANSFER LEARNING APPROACH WITH THE GRADING OF DIABETIC RETINOPATHY IN HOME-CENTRIC ENVIRONMENT. European Journal of Molecular & Clinical Medicine, 10(1), 1764-1778.
Dr.C.Saravanabhavan,MA.Swedhaa,Dr.P.Preethi,Mrs.K.M.Swarna Devi .. "RULE MINING BASED CLINICAL TRIAL USING TRANSFER LEARNING APPROACH WITH THE GRADING OF DIABETIC RETINOPATHY IN HOME-CENTRIC ENVIRONMENT". European Journal of Molecular & Clinical Medicine, 10, 1, 2023, 1764-1778.
(2023). 'RULE MINING BASED CLINICAL TRIAL USING TRANSFER LEARNING APPROACH WITH THE GRADING OF DIABETIC RETINOPATHY IN HOME-CENTRIC ENVIRONMENT', European Journal of Molecular & Clinical Medicine, 10(1), pp. 1764-1778.
RULE MINING BASED CLINICAL TRIAL USING TRANSFER LEARNING APPROACH WITH THE GRADING OF DIABETIC RETINOPATHY IN HOME-CENTRIC ENVIRONMENT. European Journal of Molecular & Clinical Medicine, 2023; 10(1): 1764-1778.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 69
  • PDF Download: 77
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  editor.ejmcm21@gmail.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus