TITLE:
Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network
AUTHORS:
Jiahe Yan, Emily Tucci, Nathaniel Jaffe
KEYWORDS:
Karyotype, t(9;22) Chromosomal Translocation, ResNet, Deep Learning
JOURNAL NAME:
Journal of Computer and Communications,
Vol.7 No.12,
December
27,
2019
ABSTRACT: Karyotype analysis has significant clinical importance. Effectively detecting the exact abnormity of chromosomes will contribute to the diagnosis of certain diseases. In this paper, I presented a convenient and reliable system that was capable of detecting t(9;22) chromosome translocation, a specific chromosomal abnormity in CML patients. The functions of this system were based on deep learning algorithms, and I created a classification system using ResNet. The model could effectively detect t(9;22) translocation based on images of chromosomes 9 and 22. This model achieves a 97.5% accuracy on the validation set.