EfficientNet-B0 outperforms other CNNs in image-based five-class embryo grading: a comparative analysis

EfficientNet-B0 outperforms other CNNs in image-based five-class embryo grading: a comparative analysis

Background: Evaluating embryo quality is crucial for the success of in vitro fertilization procedures. Traditional methods, such as the Gardner grading system, rely on subjective human assessment of morphological features, leading to potential inconsistencies and errors. Artificial intelligence-powe...

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Bibliographic Details
Main Authors: Vincent Jaehyun Shim, Hosup Shim, Sangho Roh
Format: Article
Language:English
Published: The Korean Society of Animal Reproduction and Biotechnology 2024-12-01
Series:Journal of Animal Reproduction and Biotechnology
Subjects:
blastocyst
convolutional neural networks
deep learning
embryo
in vitro fertilization
Online Access:https://www.e-jarb.org/journal/view.html?uid=2713&vmd=Full
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