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Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1988) Learning Representations by Back-Propagating Errors. Cognitive Modeling, 5, 1.

has been cited by the following article:

• 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.