Robust recognition of white blood cell images

The objective of this work is to investigate the white blood cell (WBC) image recognition problem at all stages. A robust and effective method for automatic WBC differentiation, based on both statistical pattern recognition and neural net approaches, is presented. We demonstrate well-evaluated results ranging from image scene segmentation techniques to recognition details. Recognition accuracy on the test set of 662 images of five WBC types obtained by different imaging systems from 22 bloodstains is not less than 98%.

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