Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network

• 作者: Emily Tucci Jiahe Yan Nathaniel Jaffe
• 来源期刊: 电脑和通信(英文)
• 学科: 医学
• 关键词: KARYOTYPE t(9 22) CHROMOSOMAL TRANSLOCATION ResNet DEEP LEARNING
• 年，卷（期）: 2019,（12）
• 页码范围: 102-111
• 页数: 10页
• 分类号: R73

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.