Embryo Grading after In Vitro Fertilization using YOLO

Abstract

In vitro fertilization is an implementation of Assistive Reproductive Technology. This technology can produce embryos outside the mother's womb by manipulating gametes outside the human body. The success rate of in vitro fertilization is the selection of good-grading embryos. In this study, the authors used Yolo Version 3 to perform object detection objectively by introducing grades for each embryo image. The author uses an embryo image sourced from the Indonesian Medical Education and Research Institute with information on the quality of the embryo. In this study, the author separated the data into two schemes. The first scheme separates data into training data of 70%, 15% validation data, and 15% for testing data. The second scheme uses a Stratified K-Fold Cross-Validation with a fold value =3. In training, the writer configures the values ??of Max Batches=6000, Steps=4800,5400, Batch=64, and Subdivision=16 by doing image augmentation (saturation=1.5, exposure=1.5, hue=0.1, jitter=0.3, random=1). For each of the obtained mAP (Mean Average Precision) values ??for data separation schemes, one is 100.00% in the 6000th iteration, while for the two-data separation scheme, the highest mAP is 97.33%.% in the fold=3 and 5000th iteration. It means that both separation schemes are sufficient in terms of mAP.