Improved Deep Convolutional Neural Networks via Boosting for Predicting the Quality of In Vitro Bovine Embryos

Turki Turki, Zhi Wei

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Automated diagnosis for the quality of bovine in vitro-derived embryos based on imaging data is an important research problem in developmental biology. By predicting the quality of embryos correctly, embryologists can (1) avoid the time-consuming and tedious work of subjective visual examination to assess the quality of embryos; (2) automatically perform real-time evaluation of embryos, which accelerates the examination process; and (3) possibly avoid the economic, social, and medical implications caused by poor-quality embryos. While generated embryo images provide an opportunity for analyzing such images, there is a lack of consistent noninvasive methods utilizing deep learning to assess the quality of embryos. Hence, designing high-performance deep learning algorithms is crucial for data analysts who work with embryologists. A key goal of this study is to provide advanced deep learning tools to embryologists, who would, in turn, use them as prediction calculators to evaluate the quality of embryos. The proposed deep learning approaches utilize a modified convolutional neural network, with or without boosting techniques, to improve the prediction performance. Experimental results on image data pertaining to in vitro bovine embryos show that our proposed deep learning approaches perform better than existing baseline approaches in terms of prediction performance and statistical significance.

Original languageEnglish (US)
Article number1363
JournalElectronics (Switzerland)
Volume11
Issue number9
DOIs
StatePublished - May 1 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • applications in biology and medicine
  • boosting
  • deep learning
  • developmental biology
  • reproductive biology

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