Literature survey of chromosomes classification and anomaly detection using machine learning algorithms

被引：2
|
作者：
Nimitha, N. [1 ]
Arun, C. [1 ]
Puvaneswari, A. S. [1 ]
Paninila, B. [1 ]
Pavithra, V. R. [1 ]
Pavithra, B. [1 ]
机构：
[1] RMK Coll Engn & Technol, Dept ECE, Chennai, Tamil Nadu, India
来源：
2ND INTERNATIONAL CONFERENCE ON ADVANCES IN MECHANICAL ENGINEERING (ICAME 2018) | 2018年 / 402卷
关键词：
Bilateral filter; Fuzzy C means; Feature extraction; Segmentation; Classification; Neural network;
D O I：
10.1088/1757-899X/402/1/012194
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Detection of chromosomal anomaly is done to prevent diseases at early stage. Karyotyping is the oldest manual method of detecting chromosomal abnormalities by dividing the chromosomes in laboratories. Reviews on karyotyping and previous other classification reviews on classification state that classifications were not extremely accurate. Some of them needed operator's interaction in the identification and separation of overlapping or touching chromosomes. They also didnot work properly for acrocentric, slanted, curved, banded chromosomes. Some works only for particular chromosomal anomaly. In our paper we are proposing chromosomal anomaly detection through various classification techniques to reach out the best accuracy.
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收藏
页数：7
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