Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques

Journal of Information Technology and Cyber Security Pub Date : 2024-01-31 DOI:10.30996/jitcs.8716

Adithya Kusuma Whardana, Parma Hadi Rentelinggi, Hezkiel Dokta Timothy

引用次数: 0

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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利用 CNN 和 RNN 技术检测糖尿病视网膜病变血管

本研究旨在使用卷积神经网络（CNN）和递归神经网络（RNN）检测糖尿病视网膜病变。主要目的是比较这两种方法在检测糖尿病视网膜病变方面的效果。根据每种方法训练 10 次后的研究结果，CNN 方法的准确率为 92%，RNN 方法的准确率为 50%。这些结果表明，就本研究使用的数据集而言，CNN 方法在检测糖尿病视网膜病变方面比 RNN 方法更有效。CNN 方法之所以更好，是因为它能从图像中提取空间特征，这在图像分类任务中非常重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Journal of Information Technology and Cyber Security

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