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All Journal Jupiter IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi Jurnal Komputer Terapan Jurnal Informatika Universitas Pamulang Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Kumawula: Jurnal Pengabdian Kepada Masyarakat Infotekmesin Jurnal Pengabdian UNDIKMA Jurnal Pengabdian Polinema Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika dan Multimedia Jurnal Informatika Polinema (JIP)
Wilda Imama Sabilla
Politeknik Negeri Malang
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Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image Sabilla, Wilda Imama; Hani'ah, Mamluatul; Ririd, Ariadi Retno Tri Hayati; Amalia, Astrifidha Rahma
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.4976

Abstract

Proliferative Diabetic Retinopathy (PDR) is the most severe stage of Diabetic Retinopathy (DR), carrying the highest risk of complications. Automatic detection can help provide earlier and more accurate PDR diagnosis, but prediction accuracy may decline due to limitations in retinal images. Therefore, image enhancement techniques are often applied to improve DR classification. This study aims to detect PDR from retinal images using Convolutional Neural Networks (CNNs) and to evaluate the impact of three enhancement methods. This research method is based on a CNN architecture, including ResNet34, InceptionV2, and DenseNet121, as well as enhancement methods such as CLAHE, Homomorphic Filtering (HF), and Multiscale Contrast Enhancement (MCE). The results of this research show that CNN performance varies across architectures and enhancement methods. The highest performance was achieved using ResNet34 with HF, yielding an accuracy of 0.976, precision of 0.934, and recall of 0.904. CLAHE generally improved performance across architectures, achieving the best average accuracy of 0.953, whereas MCE decreased classification accuracy. Overall, the findings highlight the importance of selecting appropriate enhancement methods to improve PDR detection accuracy. Implementing such systems in clinical screening could help reduce the risk of vision impairment among diabetic patients.
Co-Authors Amalia, Astrifidha Rahma Ariadi Retno Tri Hayati Ririd As Sidiq, Zidan Shabira Candra Bella Vista Chastine Fatichah Dhebys Suryani Hormansyah, Dhebys Suryani Dikky Rahmad Shafara Dimas Wahyu Wibowo Dwi Puspitasari Firdaus, Aji Akbar Habibie Ed Dien Habibie Ed Dien Handoyo, Irvandy Ika Kusumaning Putri Mamluatul Hani’ah Moch Zawaruddin Abdullah Nasa Zata Dina Nurhayati, Rafika Nurissaidah Ulinnuha Perkasa, Muhammad Adisa Putra Rokhimatul Wakhidah Rosa Andrie Asmara Rully Soelaiman Sarah Astiti S.Kom., M.MT Syafaat Akbar Tesa Eranti Putri Ulla Delfana Rosiani Utama, Andhika Vit Zuraida Yunita Ardilla Yuri Ariyanto Yusron, Rizqi Darma Rusdiyan