  A critical factor that influences the success of an in-vitro fertilization
(IVF) procedure is the quality of the transferred embryo. Embryo morphology
assessments, conventionally performed through manual microscopic analysis
suffer from disparities in practice, selection criteria, and subjectivity due
to the experience of the embryologist. Convolutional neural networks (CNNs) are
powerful, promising algorithms with significant potential for accurate
classifications across many object categories. Network architectures and
hyper-parameters affect the efficiency of CNNs for any given task. Here, we
evaluate multi-layered CNNs developed from scratch and popular deep-learning
architectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xception
in differentiating between embryos based on their morphological quality at 113
hours post insemination (hpi). Xception performed the best in differentiating
between the embryos based on their morphological quality.
