Using recurrent neural networks for automatic chromosome classification

被引：0

作者：

Martínez, U ^{[1
]}

Juan, A ^{[1
]}

Casacuberta, F ^{[1
]}

机构：

[1] Univ Politecn Valencia, Inst Tecnol Informat, Dept Sistemas Informat & Computac, Valencia 46020, Spain

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2002 | 2002年 / 2415卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Partial recurrent connectionist models can be used for classification of objects of variable length. In this work, an Elman network has been used for chromosome classification. Experiments were carried out using the Copenhagen data set. Local features over normal slides to the axis of the chromosomes were calculated, which produced a type of time-varying input pattern. Results showed an overall error rate of 5.7%, which is a good performance in a task which does not take into account cell context (isolated chromosome classification).

引用

页码：565 / 570

页数：6

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