Deep-learning based model for sperm morphology assessment using the SMD/MSS dataset
Our deep learning approach for sperm morphology classification enables the automation, standardization, and acceleration of semen analysis. It underscores the significance of artificial intelligence in medical applications, with a particular focus on its impact in the field of reproductive biology.
Authors
- S. Daoud (ORCID: https://orcid.org/0000-0003-4330-7363)
- Rania Maalej (ORCID: https://orcid.org/0000-0003-1657-3324)
- Afifa Sellami (ORCID: https://orcid.org/0000-0002-0347-7715)
- Tarek Rebaï (ORCID: https://orcid.org/0000-0002-8470-6494)
- Olfa Abdelkefi
Institutions
- Tunis University (TN)
- University of Sfax (TN)
- Pathways Behavioral Services (US)
- Hopital Universitaire Hedi Chaker (TN)
- Centre of Biotechnology of Sfax (TN)
- Tunis El Manar University (TN)
Publication Details
- Journal
- Future Science OA
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1080/20565623.2025.2583010
- Primary Topic
- Sperm and Testicular Function
- Type
- article
- Field-Weighted Citation Impact
- 0.00