.png){kind=icon}
.png){kind=icon}
Return
Chromosome classification using dynamic time warping
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2007.09.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A classifier based on dynamic time warping (DTW) has been developed to perform the classification of human chromosomes. DTW is used in speech recognition applications to compare two time-sequences. This paper describes a method to adapt the DTW technique in order to deal with the length and the density profile, which are common features used in classifying chromosomes. The DTW classifier is able to compare chromosomes with different elongations. Since chromosomes are non-rigid objects, the proposed classifier has the main advantage of requiring only a small training set in comparison with the conventional methods based on Bayesian classifiers or neural networks. For the same classification accuracy of 81.0%, we achieve a reduction of 88% of the size of the training set in comparison with a Bayesian classifier. (c) 2007 Elsevier B.V. All rights reserved.
Keywords:
chromosome
karyotype
feature extraction
classification
dynamic time warping
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W