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

TL;DR

This study evaluates various deep convolutional neural networks, including custom and popular architectures, for classifying human embryo images based on morphological quality to improve IVF success rates.

Contribution

It compares multiple CNN architectures, including Inception, ResNET, and Xception, for embryo quality classification, highlighting the superior performance of Xception.

Findings

Xception outperformed other architectures in embryo classification

Deep CNNs can effectively assess embryo morphological quality

Automated analysis reduces subjectivity in embryo assessment

Abstract

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.