基于深度卷积神经网络形态特征对人类胚胎图像进行分类的评估

影响体外受精（IVF）程序成功的关键因素是移植胚胎的质量。由于胚胎学家的经验，传统上通过手动显微镜分析进行的胚胎形态评估在实践，选择标准和主观性方面存在差异。..

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

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.