基于卷积神经网络的眼底图像分类

U. Savitha, Kodali Lahari Chandana, A. Cathrin Sagayam, S. Bhuvaneswari
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摘要

不同的眼病在临床上用于确定眼睛的实际状态,在治疗阶段的药物治疗和其他替代方法的结果。简单性、临床性是任何分类系统最重要的要求。在现有的研究中,他们使用不同的机器学习技术来检测单一疾病。而深度学习系统,即卷积神经网络(cnn),则可以对病眼和正常眼之间的图像进行分层表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fundus Image Classification Using Convolutional Neural Network
Different eye disease has clinical use in defining of the actual status of eye, in the outcome of the medication and other alternatives in the curative phase. Mainly simplicity, clinical nature are the most important requirements for any classification system. In the existing they used different machine learning techniques to detect only single disease. Whereas deep learning system, which is named as Convolutional neural networks (CNNs) can show hierarchical representing of images between disease eye and normal eye pattern.
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