Fertility Assessment Model For Embryo Grading Using Convolutional Neural Network (CNN)

Abstract

During an in vitro fertilization (IVF), an egg cell and sperm are combined outside of the body. The selection of embryos during IVF is very important. The quality of the embryo needs to be evaluated before it may be transferred. At this moment, the quality of embryos is evaluated visually. The morphological judgment is dependent on the expertise and experience of the attending physician or embryologist. The evaluation of embryo images can be done with the use of artificial intelligence (AI), which can be utilized to achieve unbiased automatic embryo segmentation. Both supervised and unsupervised methods can be used to complete the segmentation process. CNN is utilized in this study to perform the segmentation of embryo pictures. The model that performs the best in this research makes use of typical training data and divides it up into two classes. It has an accuracy of 93.8 percent, and by using it, the research can assess whether an embryo is usable.