Abstract
Morphological sperm analysis is one of the crucial steps in the male-based infertility diagnosis. Currently, analyses are mostly performed by visual assessment technique because of its easy implementation, quick response and cheapness properties. However, the expertise level of the observer has great importance in the visual assessment technique. Results can be different and misleading according to the observer analysis capability. Therefore, human factor should be eliminated and the analysis should be performed by an objective computerized system. In this study, we used descriptor-based features in the classification of the normal, abnormal and non-sperm patches. Additionally, we investigated the effects of two de-noising techniques in the classification performance due to the presence of noises in the patches. Results indicate that the de-noising processes have great importance in the classification performance. Moreover, a wavelet based adaptive de-noising approach dramatically increased the performance to 86% with support vector machine polynomial kernel classifier.
Original language | English |
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Title of host publication | UBMK 2018 - 3rd International Conference on Computer Science and Engineering |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 658-661 |
Number of pages | 4 |
ISBN (Electronic) | 9781538678930 |
DOIs | |
Publication status | Published - 6 Dec 2018 |
Externally published | Yes |
Event | 3rd International Conference on Computer Science and Engineering, UBMK 2018 - Sarajevo, Bosnia and Herzegovina Duration: 20 Sept 2018 → 23 Sept 2018 |
Publication series
Name | UBMK 2018 - 3rd International Conference on Computer Science and Engineering |
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Conference
Conference | 3rd International Conference on Computer Science and Engineering, UBMK 2018 |
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Country/Territory | Bosnia and Herzegovina |
City | Sarajevo |
Period | 20/09/18 → 23/09/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Adaptive Denoising
- Speed Up Robust Features
- Sperm Morphological Analysis
- Support Vector Machine