{"title":"Interpretation of Uroflow Graphs with Artificial Neural Networks","authors":"S. Altunay, Z. Telatar, O. Eroğul, E. Aydur","doi":"10.1109/SIU.2006.1659698","DOIUrl":null,"url":null,"abstract":"Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change of voided urine volume. The main purpose of the study is to evaluate uroflowmetric data using artificial neural networks (ANN) and provide a pre-diagnostic result for urology specialists. The ANN is trained using back-propagation method and the inputs of ANN are the results of a special feature extraction algorithm, which is designed with the suggestions of urology specialists. System's success is monitored with a set of data, which was already diagnosed by specialists. The outputs of ANN are classified into three groups, namely, \"healthy\", \"possible pathologic\" and \"pathologic\"","PeriodicalId":415037,"journal":{"name":"2006 IEEE 14th Signal Processing and Communications Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 14th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2006.1659698","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
Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change of voided urine volume. The main purpose of the study is to evaluate uroflowmetric data using artificial neural networks (ANN) and provide a pre-diagnostic result for urology specialists. The ANN is trained using back-propagation method and the inputs of ANN are the results of a special feature extraction algorithm, which is designed with the suggestions of urology specialists. System's success is monitored with a set of data, which was already diagnosed by specialists. The outputs of ANN are classified into three groups, namely, "healthy", "possible pathologic" and "pathologic"