{"title":"基于对偶树复小波变换的精子异常分类","authors":"Hamza Osman Ilhan, Gorkem Serbes, N. Aydin","doi":"10.1109/TSP.2018.8441431","DOIUrl":null,"url":null,"abstract":"In the proposed study, Dual Tree Complex Wavelet Transform (DTCWT) based statistical features that are derived from normal sperm, abnormal sperm and non-sperm patches are fed to Support Vector Machine classifier with the aim of three class discrimination. The obtained results are compared with the classical dyadic discrete wavelet transform and the superiority of the proposed method has been shown in terms of accuracy and F-measure metrics. The results show that higher accuracy and F-measure scores have been obtained with the proposed approach due to the shift invariance and better direction selectivity property of the DTCWT.","PeriodicalId":383018,"journal":{"name":"2018 41st International Conference on Telecommunications and Signal Processing (TSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Dual Tree Complex Wavelet Transform Based Sperm Abnormality Classification\",\"authors\":\"Hamza Osman Ilhan, Gorkem Serbes, N. Aydin\",\"doi\":\"10.1109/TSP.2018.8441431\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the proposed study, Dual Tree Complex Wavelet Transform (DTCWT) based statistical features that are derived from normal sperm, abnormal sperm and non-sperm patches are fed to Support Vector Machine classifier with the aim of three class discrimination. The obtained results are compared with the classical dyadic discrete wavelet transform and the superiority of the proposed method has been shown in terms of accuracy and F-measure metrics. The results show that higher accuracy and F-measure scores have been obtained with the proposed approach due to the shift invariance and better direction selectivity property of the DTCWT.\",\"PeriodicalId\":383018,\"journal\":{\"name\":\"2018 41st International Conference on Telecommunications and Signal Processing (TSP)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 41st International Conference on Telecommunications and Signal Processing (TSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSP.2018.8441431\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 41st International Conference on Telecommunications and Signal Processing (TSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSP.2018.8441431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
在本研究中,基于对偶树复小波变换(Dual Tree Complex Wavelet Transform, DTCWT)将正常精子、异常精子和非精子斑块的统计特征输入到支持向量机分类器中进行三类识别。将所得结果与经典的二进离散小波变换进行了比较,表明了该方法在精度和f度量指标方面的优越性。结果表明,由于DTCWT的位移不变性和更好的方向选择性,该方法获得了更高的精度和F-measure分数。
Dual Tree Complex Wavelet Transform Based Sperm Abnormality Classification
In the proposed study, Dual Tree Complex Wavelet Transform (DTCWT) based statistical features that are derived from normal sperm, abnormal sperm and non-sperm patches are fed to Support Vector Machine classifier with the aim of three class discrimination. The obtained results are compared with the classical dyadic discrete wavelet transform and the superiority of the proposed method has been shown in terms of accuracy and F-measure metrics. The results show that higher accuracy and F-measure scores have been obtained with the proposed approach due to the shift invariance and better direction selectivity property of the DTCWT.