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All Journal Journal of Information Technology and Cyber Security
Parma Hadi Rentelinggi
Keio University
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques Adithya Kusuma Whardana; Parma Hadi Rentelinggi; Hezkiel Dokta Timothy
Journal of Information Technology and Cyber Security Vol 1 No 2 (2023): July
Publisher : Department of Information Systems and Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.8716

Abstract

This research aims to detect diabetic retinopathy using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The main objective is to compare these two methods in detecting the condition. Based on the study’s result after training 10 times on each method, the accuracy results were 92% for the CNN method and 50% for the RNN method. These results show, this study with the dataset used, the CNN method is much more effective in detecting diabetic retinopathy than the RNN method. The CNN method is better due to its ability to extract spatial features from images, which is important in image classification tasks.
Co-Authors Hezkiel Dokta Timothy Whardana, Adithya Kusuma