Metaphase finding with deep convolutional neural networks

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

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

12 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)353-361
Sayfa sayısı9
DergiBiomedical Signal Processing and Control
Hacim52
DOI'lar
Yayın durumuYayınlandı - Tem 2019

Bibliyografik not

Publisher Copyright:
© 2019 Elsevier Ltd

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