A review of different deep learning techniques for sperm fertility prediction
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 acc...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-05-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2023838?viewType=HTML |
| _version_ | 1797822528350060544 |
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| author | Muhammad Suleman Muhammad Ilyas M. Ikram Ullah Lali Hafiz Tayyab Rauf Seifedine Kadry |
| author_facet | Muhammad Suleman Muhammad Ilyas M. Ikram Ullah Lali Hafiz Tayyab Rauf Seifedine Kadry |
| author_sort | Muhammad Suleman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T10:10:26Z |
| format | Article |
| id | doaj.art-4b3cb81141bd484b92baaed2c60379d9 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-13T10:10:26Z |
| publishDate | 2023-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-4b3cb81141bd484b92baaed2c60379d92023-05-22T01:44:28ZengAIMS PressAIMS Mathematics2473-69882023-05-0187163601641610.3934/math.2023838A review of different deep learning techniques for sperm fertility predictionMuhammad Suleman 0Muhammad Ilyas1M. Ikram Ullah Lali 2Hafiz Tayyab Rauf3 Seifedine Kadry41. Department of CS IT, University of Sargodha, Pakistan1. Department of CS IT, University of Sargodha, Pakistan2. Department of Information Sciences, University of Education Lahore, Pakistan3. Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK4. 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, LebanonSperm 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.https://www.aimspress.com/article/doi/10.3934/math.2023838?viewType=HTMLsperm morphologyautomatic image analysissperm defectsinfertilityconvolutional neural network (cnn)deep learning |
| spellingShingle | Muhammad Suleman Muhammad Ilyas M. Ikram Ullah Lali Hafiz Tayyab Rauf Seifedine Kadry A review of different deep learning techniques for sperm fertility prediction AIMS Mathematics sperm morphology automatic image analysis sperm defects infertility convolutional neural network (cnn) deep learning |
| title | A review of different deep learning techniques for sperm fertility prediction |
| title_full | A review of different deep learning techniques for sperm fertility prediction |
| title_fullStr | A review of different deep learning techniques for sperm fertility prediction |
| title_full_unstemmed | A review of different deep learning techniques for sperm fertility prediction |
| title_short | A review of different deep learning techniques for sperm fertility prediction |
| title_sort | review of different deep learning techniques for sperm fertility prediction |
| topic | sperm morphology automatic image analysis sperm defects infertility convolutional neural network (cnn) deep learning |
| url | https://www.aimspress.com/article/doi/10.3934/math.2023838?viewType=HTML |
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