{"title":"基于klfda的决策融合检测胃x线图像幽门螺杆菌感染","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":"{\"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}","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
摘要
提出了基于核局部Fisher判别分析(Kernel Local Fisher Discriminant Analysis, KLFDA)的决策融合方法,提高了幽门螺杆菌(Helicobacter pylori)感染检测的性能。本文最大的贡献是基于KLFDA提取更多的判别特征进行决策融合。由于本文采用的决策融合不仅考虑检测结果,而且考虑视觉特征,因此通过KLFDA计算更多的判别特征,使得更准确的决策融合成为可能。实验结果表明了该方法的有效性。
Helicobacter pylori infection detection from gastric X-ray images using KLFDA-based decision fusion
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.