Metaphase finding with deep convolutional neural networks


Moazzen Y., Çapar A., Albayrak A., Calik N., Töreyin B. U.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol.52, pp.353-361, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52
  • Publication Date: 2019
  • Doi Number: 10.1016/j.bspc.2019.04.017
  • Journal Name: BIOMEDICAL SIGNAL PROCESSING AND CONTROL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.353-361
  • Keywords: Metaphase detection, Karyotyping, Deep convolutional neural networks, CHROMOSOMES, CLASSIFICATION, IDENTIFICATION, SPREADS, NUCLEI
  • Istanbul Technical University Affiliated: Yes

Abstract

Background: Finding analyzable metaphase chromosome images is an essential step in karyotyping which is a common task for clinicians to diagnose cancers and genetic disorders precisely. This step is tedious and time-consuming. Hence developing automated fast and reliable methods to assist clinical technicians becomes indispensable. Previous approaches include methods with feature extraction followed by rule or quality based classifiers, component analysis, and neural networks.