{"title":"A neural network expert system for diagnosing eye diseases","authors":"Mostafa Mahmoud Syiam","doi":"10.1109/CAIA.1994.323624","DOIUrl":null,"url":null,"abstract":"Presents a neural network expert system to assist a GP in early medical diagnosis of eye diseases in patients. The developed system bases its diagnosis on patient symptoms and signs, and uses a multilayer feedforward network with a single hidden layer. The backpropagation algorithm is employed for training the network in a supervised mode. The effect of the number of nodes in the hidden layer on the developed system's performance is discussed. Analysis of the results indicates that the developed system has a disease diagnosis ratio of above 87 percent. To evaluate the performance of the developed system, a test data set was given to both GPs and specialists. It is indicated that the performance of the developed system exceeds that of the GPs, and it reaches the level of performance of the eye specialists.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIA.1994.323624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Presents a neural network expert system to assist a GP in early medical diagnosis of eye diseases in patients. The developed system bases its diagnosis on patient symptoms and signs, and uses a multilayer feedforward network with a single hidden layer. The backpropagation algorithm is employed for training the network in a supervised mode. The effect of the number of nodes in the hidden layer on the developed system's performance is discussed. Analysis of the results indicates that the developed system has a disease diagnosis ratio of above 87 percent. To evaluate the performance of the developed system, a test data set was given to both GPs and specialists. It is indicated that the performance of the developed system exceeds that of the GPs, and it reaches the level of performance of the eye specialists.<>
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