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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Kreativitas PKM JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Indonesian of Health Information Management Journal (INOHIM) PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan PELS (Procedia of Engineering and Life Science) Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis
Husni Abdul Muchlis
Universitas Esa Unggul
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences

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Convolutional Neural Networks-Based Deep Learning for Diabetic Retinopathy Detection Nurmalasari, Mieke; Kurniawati, Anastasia Cyntia Dewi; Herwanto, Agus; Kurniawati, Dyah; Muchlis, Husni Abdul; Pertiwi, Tria Saras
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.38631

Abstract

Diabetic retinopathy (DR) is a major complication of diabetes that can cause permanent vision loss, affecting about 35% of people with type 2 diabetes worldwide. However, existing diagnostic models often struggle with class imbalance and limited generalizability across diverse real-world datasets. Early detection is crucial, yet manual screening is time-consuming and depends on expert assessment. This study develops an automated DR diagnostic system using deep learning to classify fundus images by severity. The model uses an EfficientNetB3 CNN pretrained on ImageNet, combined with CLAHE preprocessing to enhance image contrast. The preprocessing steps include resizing, CLAHE, normalization, and data augmentation (±20° rotation, horizontal flipping, and ZCA whitening). The dataset is the Gaussian-filtered APTOS 2019 set, consisting of 2,750 images across five DR levels (0–4). The model achieved 95% training accuracy and 75% validation accuracy, with overfitting observed after epoch 14. While training performance was high, evaluation metrics (Precision, Recall, F1-Score, and AUC) indicate the need for early stopping or regularization to improve generalization. Overall, CNN-based deep learning can effectively automate DR detection, though further optimization is required for better performance on unseen data. Clinically, this automated pipeline offers a reliable decision-support tool to prioritize high-risk patients for immediate ophthalmological review
Co-Authors Ambarwati Dyah Kurniawati Herwanto, Agus Hosizah Hosizah Iqbal, Muhammad Fuad Kurniawati, Anastasia Cyntia Dewi Lestari, Betri Widya Lestari, Jayanti Menna, Yasinta Rosalia Muzdalifah, Ifah Nanda Dina Cahya Nurmalasari, Mieke Oktavianti, Putri Purba, Andrea Krisler Qomarania, Witri Uama Temesvari, Nauri Anggita Tria Saras Pertiwi Wahyu Sulistiadi Witri Zuama Qomarania Yati Maryati Yulia, Noor Yusnaeni Yusnaeni, Yusnaeni