Human infertility is considered a serious disease of the the reproductive system that affects more than 10% of couples worldwide,and more than 30% of reported cases are related to men. The crucial step in evaluating male in fertility is a semen analysis, highly dependent on sperm morphology. However,this analysis is done at the laboratory manually and depends mainly on the doctor’s experience. Besides,it is laborious, and there is also a high degree of interlaboratory variability in the results. This article proposes applying a specialized convolutional neural network architecture (U-Net),which focuses on the segmentation of sperm cells in micrographs to overcome these problems.The results showed high scores for the model segmentation metrics such as precisión (93%), IoU score (86%),and DICE score of 93%. Moreover,we can conclude that U-net architecture turned out to be a good option to carry out the segmentation of sperm cells.
| Identifer | oai:union.ndltd.org:PUCP/oai:tesis.pucp.edu.pe:20.500.12404/19908 |
| Date | 11 August 2021 |
| Creators | Melendez Melendez, Roy Kelvin |
| Contributors | Beltrán Castañón, César Armando |
| Publisher | Pontificia Universidad Católica del Perú, PE |
| Source Sets | Pontificia Universidad Católica del Perú |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess, http://creativecommons.org/licenses/by-nc-sa/2.5/pe/ |
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