期刊文献+

Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network 被引量:1

Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network
下载PDF
导出
摘要 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. 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.
机构地区 Trinity-Pawling School
出处 《Journal of Computer and Communications》 2019年第12期102-111,共10页 电脑和通信(英文)
关键词 KARYOTYPE t(9 22) CHROMOSOMAL TRANSLOCATION ResNet DEEP LEARNING Karyotype t(9 22) Chromosomal Translocation ResNet Deep Learning
  • 相关文献

同被引文献8

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部