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All Journal Jurnal Masyarakat Informatika Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration
Lalang Erawan
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Journal of Soft Computing Exploration

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Eye disease classification using deep learning convolutional neural networks Rachmawanto, Eko Hari; Sari, Christy Atika; Krismawan, Andi Danang; Erawan, Lalang; Sari, Wellia Shinta; Laksana, Deddy Award Widya; Adi, Sumarni; Yaacob, Noorayisahbe Mohd
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.493

Abstract

This study begins with the analysis of the growing challenge of accurately diagnosing eye diseases, which can lead to severe visual impairment if not identified early. To address this issue, we propose a solution using Deep Learning Convolutional Neural Networks (CNNs) enhanced by transfer learning techniques. The dataset utilized in this study comprises 4,217 images of eye diseases, categorized into four classes: Normal (1,074 images), Glaucoma (1,007 images), Cataract (1,038 images), and Diabetic Retinopathy (1,098 images). We implemented a CNN model using TensorFlow to effectively learn and classify these diseases. The evaluation results demonstrate a high accuracy of 95%, with precision and recall rates significantly varying across classes, particularly achieving 100% for Diabetic Retinopathy. These findings highlight the potential of CNNs to improve diagnostic accuracy in ophthalmology, facilitating timely interventions and enhancing patient outcomes. For future research, expanding the dataset to include a wider variety of ocular diseases and employing more sophisticated deep learning techniques could further enhance the model's performance. Integrating this model into clinical practice could significantly aid ophthalmologists in the early detection and management of eye diseases, ultimately improving patient care and reducing the burden of ocular disorders.
Co-Authors Afif, Rizal Naufal Agus Winarno Agus Winarno, Agus Agustina, Feri Ajib Susanto Ali, Rabei Raad Alzami, Farrikh Cahaya Jatmoko Candra Irawan Christy Atika Sari De Rosal Ignatius Moses Setiadi Dhia Adikusuma Adikusuma Eko Hari Rachmawanto Fahmi Amiq Heru Lestiawan Krismawan, Andi Danang Laksana, Deddy Award Widya Marshela Dinda Amalia Maulydea Ramayanti Megantara, Rama Aria Moh. Yusuf, Moh. Muslih Muslih Nur Raditya Mahardika Pratama, Reza Arista Priskila Penny Ardiana Ricardus Anggi Pramunendar Saputra, Resha Mahardhika Sinaga, Daurat Sintia Rahma Herdajanti Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sumarni Adi, Sumarni Tan Samuel Permana Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yudistira Dian Hastiti Yuventius Tyas Catur Pramudi Zaenal Arifin