Table 6. Overview of datasets and algorithms for WBC classification.

The dataset utilization, segmentation techniques, feature types, enhancements applied, and classification algorithms used in various research works on WBC classification.

Author	Database	Segmentation	Features	Enhancement	Classification
Hegde et al. (2019)	ALL-IDB1 108 images	TissueQuant algorithm	Morphological feature	Color component of RGB	TissueQuant algorithm
Liang et al. (2018)	BCCD 1238 images	Nil	Size and intensity of the nucleus	Matrix Transformation	CNN-RNN
Hegde et al. (2018)	117 images	TissueQuant	Area, perimeter, circulatory, convexity and solidity	Color contrast technique	Hybrid-classifier (SVM & NN)
Yadav, Zele & Patil (2018)	Nil	K-means Zack Algorithm	Color feature, geometric feature	Prewitt and Sobel	SVM and ANN
Di Ruberto, Loddo & Putzu (2016)	ALL-IDBII 260 images, IUMS-IDB 195 images	Pixel-based	Pixel-wise features	RGB channel	SVM
Liu & Long (2019)	76 images	Inception ResNets, ImageNet	Nil	Otsu's method and erosion operation	Augmented enhanced bagging ensemble
Vogado et al. (2018)	ALL-IDB1 108 images	AlexNet + Vgg-f	Transfer learning	Nil	SVM
Othman, Mohammed & Ali (2017)	Nil	Threshold-based	Shape, intensity and texture	GLCM	MLP-BP neural network
Zhao et al. (2017)	ALL-IDB1 108 images	Nil	PRICoLBP and PRICoLBP	Nil	Granularity feature and SVM
Agaian, Madhukar & Chronopoulos (2018)	ALL-IDB1 108 images	K-means clustering algorithm	Morphological features	L * a * b * color space	SVM