Sperm Abnormality Detection Using Sequential Deep Neural Network

Suleman Shahzad, Muhammad Ilyas, M. Ikram Ullah Lali, Hafiz Tayyab Rauf (), Seifedine Kadry and Emad Abouel Nasr
Additional contact information
Suleman Shahzad: Department of CS & IT, University of Sargodha, Sargodha 40100, Pakistan
Muhammad Ilyas: Department of CS & IT, University of Sargodha, Sargodha 40100, Pakistan
M. Ikram Ullah Lali: Department of Information Sciences, University of Education Lahore, Lahore 54770, Pakistan
Hafiz Tayyab Rauf: Independent Researcher, Bradford BD8 0HS, UK
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 94612 Kristiansand, Norway
Emad Abouel Nasr: Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia

Mathematics, 2023, vol. 11, issue 3, 1-15

Abstract: Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm.

Keywords: neural networks; sperm abnormality; sequential deep neural network; fertility prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/515/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/515/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:515-:d:1039682

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().