Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment
Journal:
arXiv
Published Date:
Jun 7, 2025
Abstract
Infertility has a considerable impact on individuals' quality of life,
affecting them socially and psychologically, with projections indicating a rise
in the upcoming years. In vitro fertilization (IVF) emerges as one of the
primary techniques within economically developed nations, employed to address
the rising problem of low fertility. Expert embryologists conventionally grade
embryos by reviewing blastocyst images to select the most optimal for transfer,
yet this process is time-consuming and lacks efficiency. Blastocyst images
provide a valuable resource for assessing embryo viability. In this study, we
introduce an explainable artificial intelligence (XAI) framework for
classifying embryos, employing a fusion of convolutional neural network (CNN)
and long short-term memory (LSTM) architecture, referred to as CNN-LSTM.
Utilizing deep learning, our model achieves high accuracy in embryo
classification while maintaining interpretability through XAI.
