An efficient transportation algorithm for automatic chromosome karyotyping
Pattern Recognition Letters
A comparative study of neural network based feature extraction paradigms
Pattern Recognition Letters
Polarity-free automatic classification of chromosomes
Computational Statistics & Data Analysis
A disagreement count scheme for inference of constrained Markov networks
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Using Recurrent Neural Networks for Automatic Chromosome Classification
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Joint Classification and Pairing of Human Chromosomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Toward a completely automatic neural-network-based human chromosomeanalysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Subspace-based prototyping and classification of chromosome images
IEEE Transactions on Image Processing
Interactive structured output prediction: application to chromosome classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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Recurrent connectionist models provide a method to represent dynamic patterns in a neural network. In this work we present a method for chromosome classification based on an almost unexplored neural network technique for this task. A partially recurrent connectionist model, the Elman network, is managed to capture the dark and light band patterns of the different classes. The proposed method is completed with the formulation of the ICC (iterative contextual classification) algorithm in order to restrict the classification to the cell context, and is applied to the neural network results. The Copenhagen data set was used in the experiments, where a cross-validation method was applied in order to obtain statistically representative results using the complete corpus. The entire system obtained very good results for this task, improving the performance of other neural network approaches.