Automatic Morphological Human Embryo Assessment Using Convolutional Neural Network and Resampling Technique

Rusnanda Farhan, Ahmad Kemal Harzif, Mgs M.Luthfi Ramadhan, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Morphological human embryo assessment is a critical process to assess human embryo quality for in vitro fertilization (IVF) treatment. Embryo morphology assessments are conventionally performed through manual microscopic analysis by embryologists. However, this process leads to a subjective result that depends on how they visually inspect the morphological structures of the embryo under microscopes. Deep learning can help with the problem of assessing the image of the human embryo. But the availability of human embryo image data is not always sufficient. This result causes a limited amount of data and imbalanced data. In this study, we propose a 2D Convolutional Neural Network (CNN) for embryo grading. In addition, we use Resampling methods and Generative Adversarial Networks (GAN) to overcome imbalanced dataset problems and improve embryo grading performance. We conducted a 10-fold cross-validation to measure model performance. Our proposed method achieved an F1-Score of 0.95, 0.81, and 0.95 for classifying embryo expansion, ICM quality, and Trophectoderm quality, respectively.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
EditorsTeddy Nurcahyadi, Yessi Jusman, Christian Blum
PublisherAmerican Institute of Physics
Edition1
ISBN (Electronic)9780735450448
DOIs
Publication statusPublished - 23 Sept 2024
Event2023 4th International Conference on Information Technology and Advanced Mechanical and Electrical Engineering, ICITAMEE 2023 - Hybrid, Yogyakarta, Indonesia
Duration: 9 Aug 202310 Aug 2023

Publication series

NameAIP Conference Proceedings
Number1
Volume3115
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2023 4th International Conference on Information Technology and Advanced Mechanical and Electrical Engineering, ICITAMEE 2023
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period9/08/2310/08/23

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