C. Martinez, A. Juan and F. Casacuberta
Using Recurrent Neural Networks for Automatic Chromosome Classification
International Conference on Artificial Neural Networks ICANN 2002

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 perfomance
in a task which does not take into account cell context (isolated
chromosome classification).