A Neural-Network-Based Approach to White Blood Cell Classification

Table 4

The comparisons of the classification rates among different classification systems.

Method	Number of types	Segmentation	Classifier	Overall rate	Number of images
Ours	5	Discriminating region	MLP	99.11%	450
Ours	5	Discriminating region	SVM	97.55%	450
Ours	5	Discriminating region	HRCNN	88.89%	450
Rezatofighi et al. [1]	5	Gram-Schmidt orthogonalization and snake	SVM	86.10%	400
Tabrizi et al. [2]	5	Gram-Schmidt orthogonalization and snake	LVQ	94.10%	400
Ghosh et al. [3]	5	Watershed	Bayes classifier	83.2%	150
Young [4]	5	Histogram threshold	Distance classifier	92.46%	199
Yampri et al. [5]	5	Automatic thresholding and adaptive contour	Minimized error	96.0%	100
Bikhet et al. [6]	5	Entropy threshold and iterative threshold	Distance classifier	90.14%	71
Piuri and Scotti [7]	5	Opening and Canny edge detector	KNN, FF-NN, and RBF	92%~82%	113