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). © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Martínez, C., Juan, A., & Casacuberta, F. (2002). Using recurrent neural networks for automatic chromosome classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 565–570). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_92
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