An Automated Blastocyst Grading System Using Convolutional Neural Network and Transfer Learning

Yusuf Abas Mohamed,Umi Kalsom Yusof,Iza Sazanita Isa, Murizah Mohd Zain

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)(2023)

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In vitro fertilization (IVF) involves collecting several mature egg samples fertilized with sperm in the IVF laboratory through a process of manual grading and selecting the healthiest blastocyst embryo through visual morphology assessment. Nevertheless, this manual process is highly subjective, prone to error and human bias, time-consuming, and can result in multiple pregnancies. Since the grading system for blastocyst embryos remains inconsistent, the successful rate of IVF remains low for achieving a healthy pregnancy. This study proposes an automated system for grading the quality of blastocyst embryos to enhance IVF selection methods using convolutional neural networks (CNN) and VGG-16 with new classification layers. The CNN model has achieved training and validation accuracy of 97% and 95%, respectively, while in the testing stage with accuracy of 90% of average precision, recall, and F1-score of 91.3%, 90%, and 90%, respectively. Additionally, VGG16 with the new classification layers achieved impressive training and validation accuracy of 99.4% and 99.5% respectively, with outstanding testing accuracy of 94% and average precision, recall, and F1-score of 94.5%, 94%, and 94%, respectively. The proposed automated system is helpful for embryologists in providing consistent blastocyst grading and selection methods in IVF process.
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