A review of different deep learning techniques for sperm fertility prediction

Muhammad Suleman; Muhammad Ilyas; M. Ikram Ullah Lali; Hafiz Tayyab Rauf; Seifedine Kadry; Muhammad Suleman; Muhammad Ilyas; M. Ikram Ullah Lali; Hafiz Tayyab Rauf; Seifedine Kadry

doi:10.3934/math.2023838

AIMS Mathematics

2023, Volume 8, Issue 7: 16360-16416. doi: 10.3934/math.2023838

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Review Special Issues

A review of different deep learning techniques for sperm fertility prediction

1.
Department of CS IT, University of Sargodha, Pakistan
2.
Department of Information Sciences, University of Education Lahore, Pakistan
3.
Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
4.
Department of Applied Data Science, Noroff University College, Kristiansand, Norway
5.
Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
6.
Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

Received: 02 January 2023 Revised: 15 April 2023 Accepted: 17 April 2023 Published: 08 May 2023
MSC : 68T07

Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields.
- sperm morphology,
- automatic image analysis,
- sperm defects,
- infertility, Convolutional Neural Network (CNN), deep learning
Citation: Muhammad Suleman, Muhammad Ilyas, M. Ikram Ullah Lali, Hafiz Tayyab Rauf, Seifedine Kadry. A review of different deep learning techniques for sperm fertility prediction[J]. AIMS Mathematics, 2023, 8(7): 16360-16416. doi: 10.3934/math.2023838

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Abstract

Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields.

References

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