Multi-class Classification Approach for Retinal Diseases

Pub Date : 2023-01-01 DOI:10.12720/jait.14.3.392-398

Mario G. Gualsaqui, Stefany M. Cuenca, Ibeth L. Rosero, D. A. Almeida, C. Cadena, Fernando Villalba, Jonathan D. Cruz

引用次数: 1

Abstract

—Early detection of the diagnosis of some diseases in the retina of the eye can improve the chances of cure and also prevent blindness. In this study, a Convolutional Neural Network (CNN) with different architectures (Scratch Model, GoogleNet, VGG, ResNet, MobileNet and DenseNet) was created to make a comparison between them and find the one with the best percentage of accuracy and less loss to generate the model for a better automatic classification of images using a MURED database containing retinal images already labeled previously with their respective disease. The results show that the model with the ResNet architecture variant InceptionResNetV2 has an accuracy of 49.85%.

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视网膜疾病的多分类方法

-对某些视网膜疾病的早期发现诊断可以提高治愈的机会，也可以预防失明。在本研究中，我们创建了一个具有不同架构(Scratch Model、GoogleNet、VGG、ResNet、MobileNet和DenseNet)的卷积神经网络(CNN)，对它们进行比较，找到准确率最高、损失最小的一个，并生成模型，以便使用包含先前已标记为各自疾病的视网膜图像的MURED数据库对图像进行更好的自动分类。结果表明，采用ResNet架构变体InceptionResNetV2的模型准确率为49.85%。

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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