{"title":"Computer Aided Medical Diagnosis for the Identification of Malaria Parasites","authors":"S.F. Toha, U. K. Ngah","doi":"10.1109/ICSCN.2007.350655","DOIUrl":null,"url":null,"abstract":"This paper presents one of the applications of digital image processing in artificial intelligence particularly in the field of medical diagnosis system. Currently in Malaysia the traditional method for the identification of Malaria parasites requires a trained technologist to manually examine and detect the number of the parasites subsequently by reading the slides. This is a very time consuming process, causes operator fatigue and is prone to human errors and inconsistency. An automated system is therefore needed to complete as much work as possible for the identification of Malaria parasites. The integration both soft computing tools has been successfully designed with the capability to improve the quality of the image, analyze and classify the image as well as calculating the number of Malaria parasites","PeriodicalId":257948,"journal":{"name":"2007 International Conference on Signal Processing, Communications and Networking","volume":"82 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Signal Processing, Communications and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCN.2007.350655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32
Abstract
This paper presents one of the applications of digital image processing in artificial intelligence particularly in the field of medical diagnosis system. Currently in Malaysia the traditional method for the identification of Malaria parasites requires a trained technologist to manually examine and detect the number of the parasites subsequently by reading the slides. This is a very time consuming process, causes operator fatigue and is prone to human errors and inconsistency. An automated system is therefore needed to complete as much work as possible for the identification of Malaria parasites. The integration both soft computing tools has been successfully designed with the capability to improve the quality of the image, analyze and classify the image as well as calculating the number of Malaria parasites