Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm

Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally...

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Veröffentlicht in:	Bioengineering (Basel) Jg. 11; H. 10; S. 1044
Hauptverfasser:	Mazroa, Alanoud Al, Maashi, Mashael, Said, Yahia, Maray, Mohammed, Alzahrani, Ahmad A., Alkharashi, Abdulwhab, Al-Sharafi, Ali M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.10.2024 MDPI
Schlagworte:	Accuracy Algorithms Analysis Anomalies Art techniques Artificial intelligence boosted dipper-throated optimization Classification Computer vision Deep learning Developmental biology embryo development Embryonic development Embryos Fertility Health aspects Image filters image preprocessing In vitro fertilization Infertility Machine learning Mathematical optimization Methods Morphology Neural networks Observational learning Optimization Prevention Problem solving Reproductive technologies Risk factors swin transformer Saudi Arabia boosted dipper-throated optimization embryo development image preprocessing swin transformer computer vision
ISSN:	2306-5354, 2306-5354
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Abstract	Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.
AbstractList	Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques. Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.
Audience	Academic
Author	Maashi, Mashael Maray, Mohammed Alzahrani, Ahmad A. Mazroa, Alanoud Al Al-Sharafi, Ali M. Alkharashi, Abdulwhab Said, Yahia
AuthorAffiliation	1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia; asalmazroa@pnu.edu.sa 7 Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia 3 Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia 5 Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura University, Makkah 24382, Saudi Arabia 6 Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia 4 Department of Information Systems, College of Computer Science, King Khalid University, Abha 62521, Saudi Arabia 2 Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia
AuthorAffiliation_xml	– name: 2 Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia – name: 7 Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia – name: 6 Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia – name: 3 Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia – name: 1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia; asalmazroa@pnu.edu.sa – name: 5 Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura University, Makkah 24382, Saudi Arabia – name: 4 Department of Information Systems, College of Computer Science, King Khalid University, Abha 62521, Saudi Arabia
Author_xml	– sequence: 1 givenname: Alanoud Al orcidid: 0000-0002-6201-0410 surname: Mazroa fullname: Mazroa, Alanoud Al – sequence: 2 givenname: Mashael orcidid: 0000-0003-0446-5430 surname: Maashi fullname: Maashi, Mashael – sequence: 3 givenname: Yahia orcidid: 0000-0003-0613-4037 surname: Said fullname: Said, Yahia – sequence: 4 givenname: Mohammed orcidid: 0000-0002-7066-2945 surname: Maray fullname: Maray, Mohammed – sequence: 5 givenname: Ahmad A. orcidid: 0000-0003-1573-0367 surname: Alzahrani fullname: Alzahrani, Ahmad A. – sequence: 6 givenname: Abdulwhab surname: Alkharashi fullname: Alkharashi, Abdulwhab – sequence: 7 givenname: Ali M. orcidid: 0009-0000-0938-4969 surname: Al-Sharafi fullname: Al-Sharafi, Ali M.
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Cites_doi	10.1016/j.media.2024.103229 10.1016/j.compbiomed.2024.109000 10.1016/j.compbiomed.2023.107741 10.1038/s41598-020-61357-9 10.1038/s41556-024-01367-1 10.1109/JBHI.2023.3328954 10.1038/s42003-021-01937-1 10.1016/j.artmed.2024.102773 10.1016/j.ptlrs.2024.07.001 10.1109/TMI.2021.3116986 10.1016/j.rbmo.2020.07.003 10.7554/eLife.55301 10.1002/aisy.202300366 10.3934/math.2024500 10.3389/fendo.2023.1238251 10.1016/j.sasc.2024.200110 10.4018/979-8-3693-2105-8.ch015 10.1016/j.xhgg.2024.100312 10.1093/biolre/ioae056 10.1109/ICAPAI61893.2024.10541175 10.1093/humrep/deaa013 10.3390/ijerph20032377 10.1371/journal.pone.0262661 10.1101/2024.03.04.583269 10.1016/j.rbmo.2021.11.003 10.1109/ACCESS.2019.2937765 10.1016/j.heliyon.2024.e26415 10.1038/s41556-023-01341-3 10.1016/j.bspc.2021.102943 10.1109/TUFFC.2023.3311879 10.1007/978-981-99-8141-0_10 10.1016/j.heliyon.2021.e06298
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Snippet	Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF)...
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SubjectTerms	Accuracy Algorithms Analysis Anomalies Art techniques Artificial intelligence boosted dipper-throated optimization Classification Computer vision Deep learning Developmental biology embryo development Embryonic development Embryos Fertility Health aspects Image filters image preprocessing In vitro fertilization Infertility Machine learning Mathematical optimization Methods Morphology Neural networks Observational learning Optimization Prevention Problem solving Reproductive technologies Risk factors swin transformer
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Title	Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm
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