Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks.
Aim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cel...
| Publicado en: | Medical Records Vol. 5; no. 3; pp. 489 - 496 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Medical Records
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172831639&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172831639 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26874555 N21P jtl: Medical Records issn: 26874555 maglogo: N pubinfo: dt: 2023 vid: 5 iid: 3 pid: 71272 pub: Medical Records artinfo: ui: 172831639 172831639 172831639 10.37990/medr.1292782 172831639 ppf: 489 ppct: 7 formats: tig: atl: Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks. aug: au: Cavusoglu, Turker Gokhan, Aylin Sirin, Cansin Tomruk, Canberk Kilic, Kubilay Dogan Olmez, Emre Er, Orhan Gullu, Mehmet Kemal affil: İzmir Bakırçay University, Faculty of Medicine, Department of Histology and Embryology, İzmir, Türkiye sug: subj: Convolutional Neural Networks Utilization Ovum Classification Fertilization in Vitro Methods Fetal Development Reproduction Techniques Methods Deep Learning Methods Animal Studies Cattle Professional Knowledge Algorithms Descriptive Statistics Ovary Models, Biological ab: Aim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods. Material and Methods: In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse. Results: The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset. Conclusion: Continuous research and optimization of the model can further improve its performance and benefit the field of cumulusoocyte complexes classification and oocyte quality assessment. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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