Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques

Authors

DOI:

https://doi.org/10.30996/jitcs.8716

Keywords:

Blood vessel detection, Convolutional Neural Network, deep learning, diabetic retinopathy, medical image analysis, Recurrent Neural Network

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.

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Author Biographies

Adithya Kusuma Whardana, Universitas Tanri Abeng

Department of Informatics Engineering

Parma Hadi Rentelinggi, Keio University

Department of Information and Computer Science

Hezkiel Dokta Timothy, Universitas Tanri Abeng

Department of Informatics Engineering

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Published

2023-12-31

How to Cite

Whardana, A. K., Rentelinggi, P. H., & Timothy, H. D. (2023). Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques. Journal of Information Technology and Cyber Security, 1(2), 68–75. https://doi.org/10.30996/jitcs.8716

Issue

Vol. 1 No. 2 (2023): July

Section

Research Article

License

Copyright (c) 2023 The Author(s)

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