Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Table 8

Comparison of classification accuracy, AUC, and FP/image values from different approaches in breast cancer diagnosis.

Author	Database	Method	Classifier	Result	AUC	FP/image
Eltoukhy et al. [33]	MIAS	Biggest curvelet coefficients as a feature vector	Euclidean classifier	94.07%	—	—
Eltoukhy et al. [42]				98.59	—	—
Eltoukhy et al. [8]			SVM	97.3	—	—
Dhahbi et al. [34]	Mini-MIAS	Curvelet moments	KNN	91.27	—	—
	DDSM			86.46	—	—
Bruno et al. [4]	DDSM	Curvelet + LBP	SVM	85	0.85	—
			PL	94	0.94	—
da Rocha et al. [40]	DDSM	LBP	SVM	88.31	0.88	—
Kanadam and Chereddy [3]	MIAS	Sparse ROI	SVM	97.42	—	—
Pereira et al. [18]	DDSM	Wavelet and Wiener filter	Multiple thresholding, wavelet, and GA	—	—	1.37
Liu and Zeng [29]	DDSM, FFDM	GLCM, CLBP, and geometric features	SVM	—	—	1.48
De Sampaio et al. [39]	DDSM	LBP	DBSCAN	98.26		0.19
Zyout et al. [30]	DDSM	Second order statistics of wavelet coefficients (SOSWC)	SVM	96.8	0.97	0.018
	MIAS			95.2	96.6	0.029
Casti et al. [31]	DDSM	Differential features	Fisher linear discriminant analysis (FLDA)	—	—	1.68
	MIAS					2.12
	FFDM					0.82
Proposed method	MIAS	LBP based on sparse curvelet subband coefficients	ANN	98.57	0.98	0.01
	DDSM			98.70	0.98	0.03
	TMCH: Scanner1			98.30	0.98	0.05
	TMCH: Scanner2			100	1	0