Dual Tree Complex Wavelet Transform Based Sperm Abnormality Classification

Hamza Osman Ilhan, Gorkem Serbes, Nizamettin Aydin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018
EditorsNorbert Herencsar
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538646953
DOIs
Publication statusPublished - 20 Aug 2018
Externally publishedYes
Event41st International Conference on Telecommunications and Signal Processing, TSP 2018 - Athens, Greece
Duration: 4 Jul 20186 Jul 2018

Publication series

Name2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018

Conference

Conference41st International Conference on Telecommunications and Signal Processing, TSP 2018
Country/TerritoryGreece
CityAthens
Period4/07/186/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Discrete Wavelet Transform
  • Dual Tree Complex Wavelet Transform
  • Sperm Abnormality Classification
  • Support Vector Machines

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