Automated Detection of Human Blastocyst Quality Using Convolutional Neural Network and Edge Detector

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

IVF (in vitro fertilization) is one type of assisted reproduction technology (ART) that can be a hope for couples with fertility problems (infertility) to get progeny. In supporting the success of IVF, there are several factors that can be an important role the one of which is in determining the quality of the embryo to be implantation. There are several numbers of previous researchers who had conducted research on determining the quality of the embryo but were still assisted by an embryologist and not automatically can detect the grade of embryo quality. In this paper, we propose a Convolutional Neural Network (CNN) model using image processing for detection quality of blastocyst grade with automatically and improve the accuracy. Keras is used for the implementation of CNN. We have tested our model and have been able to achieve a detection accuracy of 64.29% without image pre-processing and 84.62% using image pre-processing with Canny edge detector.

Original languageEnglish
Title of host publication2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages181-184
Number of pages4
ISBN (Electronic)9781728114729
DOIs
Publication statusPublished - Aug 2019
Event1st International Conference on Cybernetics and Intelligent System, ICORIS 2019 - Denpasar, Bali, Indonesia
Duration: 22 Aug 201923 Aug 2019

Publication series

Name2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019

Conference

Conference1st International Conference on Cybernetics and Intelligent System, ICORIS 2019
CountryIndonesia
CityDenpasar, Bali
Period22/08/1923/08/19

Keywords

  • CNN
  • edge detector
  • human blastocyst
  • quality detection

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    Irmawati, Basari, & Gunawan, D. (2019). Automated Detection of Human Blastocyst Quality Using Convolutional Neural Network and Edge Detector. In 2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019 (pp. 181-184). [8874925] (2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICORIS.2019.8874925