{"title":"基于小波神经网络的视网膜病变疾病自动识别","authors":"F. Yagmur, B. Karlik, A. Okatan","doi":"10.1109/ICADIWT.2008.4664391","DOIUrl":null,"url":null,"abstract":"In this study, recognition of five types of retina disorders and normal retina has been studied. The names of these five different Retinopathies are: Diabetic Retinopathy, Hypertensive retinopathy, Macular Degeneration, Vein Branch Oclusion, Vitreus hemorrhage, and normal retina. A wavelet based neural network architecture has been used to diagnose retinopathy automatically. In the process, the retina images were pre-processed and resized. Later, feature extraction has been done before applying into classifier. The performance of proposed method has been found very high. The recognition rates were found %50, %70, %83, %90, %93 and %95 for testing five retinopathy cases respectively.","PeriodicalId":189871,"journal":{"name":"2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Automatic recognition of retinopathy diseases by using wavelet based neural network\",\"authors\":\"F. Yagmur, B. Karlik, A. Okatan\",\"doi\":\"10.1109/ICADIWT.2008.4664391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, recognition of five types of retina disorders and normal retina has been studied. The names of these five different Retinopathies are: Diabetic Retinopathy, Hypertensive retinopathy, Macular Degeneration, Vein Branch Oclusion, Vitreus hemorrhage, and normal retina. A wavelet based neural network architecture has been used to diagnose retinopathy automatically. In the process, the retina images were pre-processed and resized. Later, feature extraction has been done before applying into classifier. The performance of proposed method has been found very high. The recognition rates were found %50, %70, %83, %90, %93 and %95 for testing five retinopathy cases respectively.\",\"PeriodicalId\":189871,\"journal\":{\"name\":\"2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICADIWT.2008.4664391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICADIWT.2008.4664391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic recognition of retinopathy diseases by using wavelet based neural network
In this study, recognition of five types of retina disorders and normal retina has been studied. The names of these five different Retinopathies are: Diabetic Retinopathy, Hypertensive retinopathy, Macular Degeneration, Vein Branch Oclusion, Vitreus hemorrhage, and normal retina. A wavelet based neural network architecture has been used to diagnose retinopathy automatically. In the process, the retina images were pre-processed and resized. Later, feature extraction has been done before applying into classifier. The performance of proposed method has been found very high. The recognition rates were found %50, %70, %83, %90, %93 and %95 for testing five retinopathy cases respectively.