1. Metaphase finding with deep convolutional neural networks.
- Author
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Moazzen, Yaser, Çapar, Abdulkerim, Albayrak, Abdulkadir, Çalık, Nurullah, and Töreyin, Behçet Uğur
- Subjects
ARTIFICIAL neural networks ,CHROMOSOME analysis ,FEATURE extraction ,IMAGE processing ,GENETIC disorders - Abstract
• Karyotyping is a popular method of chromosome analysis in cytogenetic laboratories to diagnose the genetic disorders. • Finding analyzable metaphase images is essential for karyotyping that needs to be done automatically and accurately. • Automation of finding analyzable metaphase images reduces the interobservor variation and speeds-up the karyotyping. • Enhancing the sensitivity and specificity of this task, has big effect on overal accuracy of karyotyping resulting to the more accurate diagnosis. • An automated high accurate metaphase finder decreases the clinical and sampling work and costs. 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. 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. The proposed scheme has a 99.33% true positive rate and 0.34% of the false positive rate of metaphase finding. This study demonstrates an effective scheme for the automated finding of analyzable metaphase images with high True positive and low False positive rates. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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