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

arXiv: https://arxiv.org/abs/2005.10912 · HTML: https://mlsanity.com/paper/2005.10912/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 4 of 4)

- [Merging synthetic and real embryo data for advanced AI predictions](https://mlsanity.com/paper/2412.01255/) (2024)
- [Exploring the Role of Explainability in AI-Assisted Embryo Selection](https://mlsanity.com/paper/2308.02534/) (2023)
- [Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review](https://mlsanity.com/paper/2505.20306/) (2025)
- [Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment](https://mlsanity.com/paper/2506.06680/) (2025)

## Abstract (arXiv)

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.