Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation

2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) Pub Date : 2017-11-01 DOI:10.1109/ICITISEE.2017.8285522

A. Damayanti

{"title":"Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation","authors":"A. Damayanti","doi":"10.1109/ICITISEE.2017.8285522","DOIUrl":null,"url":null,"abstract":"The human eye is a complex organ that is essential for everyday life. The fundus is the inner surface of the eye, which lies contrary to the lens. The results of eye fundus shooting can be used to diagnose abnormalities that occur in the eye. Artificial neural networks and fuzzy systems are methods that can be used in the classification process. In this research used Levenberg-Marquardt (LM), adaptive neuro-fuzzy inference system (ANFIS), and fuzzy learning vector quantization (FLVQ) method in ANFIS clustering process for classification of retinal abdominal eye disease, Age-Related Macular Degeneration, and normal, with an input of energy coefficient, resulting from wavelet transformation process. From the results of the percentage of success of the system in the classification of disease in the eye fundus image, it appears that the system has been able to recognize the image pattern well, that is for ANFIS with lr = 0.4, mc = 0.9 is 100%, for ANFIS-FLVQ with lr = 0.9, mc = 0.1 is 100% and for LM with μ = 0.01 is 100%.","PeriodicalId":130873,"journal":{"name":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITISEE.2017.8285522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

Abstract

The human eye is a complex organ that is essential for everyday life. The fundus is the inner surface of the eye, which lies contrary to the lens. The results of eye fundus shooting can be used to diagnose abnormalities that occur in the eye. Artificial neural networks and fuzzy systems are methods that can be used in the classification process. In this research used Levenberg-Marquardt (LM), adaptive neuro-fuzzy inference system (ANFIS), and fuzzy learning vector quantization (FLVQ) method in ANFIS clustering process for classification of retinal abdominal eye disease, Age-Related Macular Degeneration, and normal, with an input of energy coefficient, resulting from wavelet transformation process. From the results of the percentage of success of the system in the classification of disease in the eye fundus image, it appears that the system has been able to recognize the image pattern well, that is for ANFIS with lr = 0.4, mc = 0.9 is 100%, for ANFIS-FLVQ with lr = 0.9, mc = 0.1 is 100% and for LM with μ = 0.01 is 100%.

查看原文本刊更多论文

基于小波变换的模糊学习向量量化、神经网络和模糊系统眼底图像分类

人眼是一个复杂的器官，对日常生活至关重要。眼底是眼睛的内表面，与晶状体相对。眼底拍摄的结果可以用来诊断眼睛发生的异常。人工神经网络和模糊系统是可以用于分类过程的方法。本研究采用Levenberg-Marquardt (LM)、自适应神经模糊推理系统(ANFIS)和模糊学习向量量化(FLVQ)方法在ANFIS聚类过程中对视网膜腹部眼病、年龄相关性黄斑变性和正常进行分类，输入小波变换后的能量系数。从系统对眼底图像疾病分类的成功率来看，系统能够很好地识别图像模式，即对于lr = 0.4的ANFIS, mc = 0.9为100%，对于lr = 0.9的ANFIS- flvq, mc = 0.1为100%，对于μ = 0.01的LM为100%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

自引率

0.00%

发文量