{"title":"On knowledge-based improvement of biomedical pattern recognition-a case study","authors":"Q. Wu, P. Suetens, A. Oosterlinck","doi":"10.1109/CAIA.1989.49159","DOIUrl":null,"url":null,"abstract":"Most biomedical pattern recognition (BPR) systems use the classical statistical pattern recognition strategy in which a feature hyperspace is constructed for the problem followed by a statistical discriminant analysis procedure. It is shown that there are several essential drawbacks with this conventional approach. These limitations often lead to highly erroneous classification results subject to time-consuming interactive corrections in existing systems. To overcome such limitations, a pilot study on applying knowledge-based techniques to chromosome classification has been carried out. The scheme developed as a result of this case study is described in detail. The system has been implemented and tested on metaphase imagery in preliminary experiments, the results of which are also presented.<<ETX>>","PeriodicalId":431245,"journal":{"name":"[1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications","volume":"407 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIA.1989.49159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
Most biomedical pattern recognition (BPR) systems use the classical statistical pattern recognition strategy in which a feature hyperspace is constructed for the problem followed by a statistical discriminant analysis procedure. It is shown that there are several essential drawbacks with this conventional approach. These limitations often lead to highly erroneous classification results subject to time-consuming interactive corrections in existing systems. To overcome such limitations, a pilot study on applying knowledge-based techniques to chromosome classification has been carried out. The scheme developed as a result of this case study is described in detail. The system has been implemented and tested on metaphase imagery in preliminary experiments, the results of which are also presented.<>