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 language | English |
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Title of host publication | 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018 |
Editors | Norbert Herencsar |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Print) | 9781538646953 |
DOIs | |
Publication status | Published - 20 Aug 2018 |
Externally published | Yes |
Event | 41st International Conference on Telecommunications and Signal Processing, TSP 2018 - Athens, Greece Duration: 4 Jul 2018 → 6 Jul 2018 |
Publication series
Name | 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018 |
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Conference
Conference | 41st International Conference on Telecommunications and Signal Processing, TSP 2018 |
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Country/Territory | Greece |
City | Athens |
Period | 4/07/18 → 6/07/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Discrete Wavelet Transform
- Dual Tree Complex Wavelet Transform
- Sperm Abnormality Classification
- Support Vector Machines