Prolificacy Assessment of Spermatozoan via state-of-the-art Deep Learning Frameworks

Satish Chandra, Mahendra Kumar Gourisaria, GM Harshvardhan, Debanjan Konar, Xin Gao, Tianyang Wang, Min Xu

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Childlessness or infertility among couples has become a global health concern. Due to the rise in infertility, couples are looking for medical supports to attain reproduction. This paper deals with diagnosing infertility among men and the major factor in diagnosing infertility among men is the Sperm Morphology Analysis (SMA). In this manuscript, we explore establishing deep learning frameworks to automate the classification problem in the fertilization of sperm cells. We investigate the performance of multiple state-of-the-art deep neural networks on the MHSMA dataset. The experimental results demonstrate that the deep learning-based framework outperforms human experts on sperm classification in terms of accuracy, throughput and reliability. We further analyse the sperm cell data by visualizing the feature activations of the deep learning models, providing a new perspective to understand the data. Finally, a comprehensive analysis is also demonstrated on the experimental results obtained and attributing them to pertinent reasons.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Access
StatePublished - 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-02-01
Acknowledged KAUST grant number(s): OSR, URF/1/2602-01, URF/1/3007-01
Acknowledgements: This work was partially supported by U.S. NIH grants R01GM134020 and P41GM103712, NSF grants DBI-1949629 and IIS-2007595,vand Mark Foundation For Cancer Research 19-044-ASP. XG acknowledges partial support by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/2602-01 and URF/1/3007-01. The computational resources was supported in part by AMD COVID-19 HPC Fund.

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science


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