Academia Open
Vol 10 No 1 (2025): June (In Progress)
Medicine

Information Systems for Convolutional Neural Network in Diabetes Detection Using Fundus Images
Sistem Informasi Jaringan Syaraf Tiruan untuk Deteksi Diabetes Menggunakan Citra Fundus


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  • Wafaa Razzaq *
Wafaa Razzaq
College of Nursing, University of Thi-Qar, Nasiriyah, Iraq , Iraq *

(*) Corresponding Author
Picture in here are illustration from public domain image or provided by the author, as part of their works
DOI: https://doi.org/10.21070/acopen.10.2025.10546
Published February 4, 2025
Keywords
  • Convolutional Neural Network,
  • Diabetes Screening,
  • Machine Learning,
  • Deep Learning
How to Cite
Razzaq , W. (2025). Information Systems for Convolutional Neural Network in Diabetes Detection Using Fundus Images. Academia Open, 10(1), 10.21070/acopen.10.2025.10546. https://doi.org/10.21070/acopen.10.2025.10546

Copyright (c) 2025 Wafaa Razzaq

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Diabetes is regarded as a serious illness, and early detection enhances patient treatment and quality of life. This research develops a convolutional neural network for diabetes detection using fundus images from the IDRID image set. The convolutional neural network's performance analysis is applied to cross-validation and comparison with other patterns found in earlier research using techniques including Random Forest, Extra Trees, SVM, and AdaBoost. Our CNN pattern consists of an input layer, three convolutional layers, three pooling layers, two fully connected layers, and an output layer with two neurons. An accuracy of 83.50% was obtained from the evaluation of the convolutional neural network pattern using 103 photos, which is better than the results of earlier research.

Highlights:

  1. CNN developed for diabetes detection using IDRiD fundus images.
  2. Compared with RF, ET, SVM, and AdaBoost via cross-validation.
  3. CNN achieved 83.50% accuracy, outperforming previous research.

Keywords: Convolutional Neural Network, Diabetes Screening, Machine Learning, Deep Learning.

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