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- W1264273296 abstract "Numbers of white blood cells in different classes help doctors to diagnose patients. A new set of features based on the mathematical morphology in the white blood cell classification problem is proposed in this paper. The proposed features are the maximum value of a pattern spectrum, the iocation where the maximum value of a pattern spectrum occurs, the first and second granulometric moments. We also propose a method to unbias neural networks by biasing the desired output using a priori information of the number of samples in each class. Regular artificial neural networks and the biased-output neural networks are applied in the experiments using the five-fold cross validation as the testing method. The results show the good performances of classifiers using our biased-output neural networks and our proposed morphology-based features." @default.
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- W1264273296 date "2003-01-01" @default.
- W1264273296 modified "2023-09-23" @default.
- W1264273296 title "Automatic White Blood Cell Classification Using Biased-Output Neural Networks with Morphological Features" @default.
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