{"title":"Helicobacter pylori infection detection from gastric X-ray images using KLFDA-based decision fusion","authors":"Kenta Ishihara, Takahiro Ogawa, M. Haseyama","doi":"10.1109/GCCE.2015.7398563","DOIUrl":null,"url":null,"abstract":"This paper presents the performance improvement of Helicobacter pylori (H. pylori) infection detection using Kernel Local Fisher Discriminant Analysis (KLFDA)-based decision fusion. As the biggest contribution of this paper, the proposed method extracts more discriminative features based on KLFDA for the decision fusion. Since the decision fusion employed in this paper can consider not only the detection results but also the visual features, by calculating more discriminative features via KLFDA, more accurate decision fusion becomes feasible. Furthermore, experimental results show the effectiveness of the proposed method.","PeriodicalId":363743,"journal":{"name":"2015 IEEE 4th Global Conference on Consumer Electronics (GCCE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 4th Global Conference on Consumer Electronics (GCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2015.7398563","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents the performance improvement of Helicobacter pylori (H. pylori) infection detection using Kernel Local Fisher Discriminant Analysis (KLFDA)-based decision fusion. As the biggest contribution of this paper, the proposed method extracts more discriminative features based on KLFDA for the decision fusion. Since the decision fusion employed in this paper can consider not only the detection results but also the visual features, by calculating more discriminative features via KLFDA, more accurate decision fusion becomes feasible. Furthermore, experimental results show the effectiveness of the proposed method.