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A critical factor that influences the success of an in-vitro fertilization(IVF) procedure is the quality of the transferred embryo. Embryo morphologyassessments, conventionally performed through manual microscopic analysissuffer from disparities in practice, selection criteria, and subjectivity dueto the experience of the embryologist. Convolutional neural networks (CNNs) arepowerful, promising algorithms with significant potential for accurateclassifications across many object categories. Network architectures andhyper-parameters affect the efficiency of CNNs for any given task. Here, weevaluate multi-layered CNNs developed from scratch and popular deep-learningarchitectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xceptionin differentiating between embryos based on their morphological quality at 113hours post insemination (hpi). Xception performed the best in differentiatingbetween the embryos based on their morphological quality.

Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality