Table 1.

Summary of some mammogram based CAD systems

Methods	Feature Vectors	Classifier	Database
Yu and Guan [51], 2000	Statistical features	MLFFNN	Nijmegen
			Database
Verma and Zakos [47], 2001	Entropy, standard deviation	BPNN	Nijmegen
	and number of pixels		Database
Bagui et al. [4], 2003	Radius, texture, perimeter,	k-NN	WDBC, WBC
	area, smoothness, compactness,
	concavity, concave points,
	symmetry, fractal dimensions
Fu et al. [17], 2005	Texture, spatial and	SVM	Nijmegen
	spectral domain		Database
Hwang and Kim [23], 2006	Zernike moments	–	–
Oliver et al. [35], 2007	Local binary patterns (LBP)	SVM	DDSM
Jasmine et al. [24], 2007	2D Wavelet transform	–	MIAS
Xu et al. [50], 2007	DWT	MLP	Private
Zhang et al. [52], 2008	Contrast, energy, entropy,	BPNN
	standard deviation, skew,
	difference, average bound gray,
	average gray level,
	no. of pixels, and
	average histogram
Rabottino et al. [38], 2008	Shape and texture features	Fuzzy classifier	DDSM
Mencattini [31], 2009	Shape features	–	DDSM
Wang et al. [48], 2009	Zernike moment-mode	–	–
	shape descriptor
Rejani and Selvi [40], (2009)	Shape feature	SVM	MIAS
Alofe et al. [1], (2009)	Wavelet, first and second	SVM	MIAS
	order statistics, shape and
	fractal dimensional features
Mazurowski et al. [30], 2011	Histogram		DDSM, DBT
Zhang et al. [53], 2011	Shape features	k-NN	MIAS, DDSM
Xu et al. [49], 2011	Statistical and shape features	–	DDSM
Tahmasbi et al. [43], 2011	Shape features	MLP	MIAS
Oliveira et al. [33], 2011	Texture features	SVM	IRMA
Balakumaran and Vennila [5], 2011	Skewness, kurtosis and	–	DDSM
	wavelet transform
Deserno et al. [11], 2012	2DPCA	SVM	IRMA
Dheeba and Tamil [12], 2010	Gabor features	RBFNN	MIAS
Oliver [35], 2012	Local features based	Gentle boost	MIAS
	on morphology	Classifier
Kabbadj et al. [25], 2012	Statistical and geometric features	SVM	MIAS
Shanthi and Bhaskaran [41], 2012	Mean, standard deviation,	Self-adaptive resource	MIAS
	skewness, kurtosis, entropy	Allocation network
Cabrera et al. [20], 2012	Texture features	–	DDSM