Metaphase finding with deep convolutional neural networks

Yaser Moazzen*, Abdulkerim Çapar, Abdulkadir Albayrak, Nurullah Çalık, Behçet Uğur Töreyin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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. Methods: A two-stage automated metaphase-finding scheme, consisting of an image processing based metaphase detection stage, and a deep convolutional neural network based selection stage is proposed. The first stage detects metaphase images from 10× scan of specimen slides. The selection stage, on the other hand, selects the analyzable ones among them. Results: The proposed scheme has a 99.33% true positive rate and 0.34% of the false positive rate of metaphase finding. Conclusion: This study demonstrates an effective scheme for the automated finding of analyzable metaphase images with high True positive and low False positive rates.

Original languageEnglish
Pages (from-to)353-361
Number of pages9
JournalBiomedical Signal Processing and Control
Publication statusPublished - Jul 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd


  • Deep convolutional neural networks
  • Karyotyping
  • Metaphase detection


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