Journal of Healthcare Engineering / 2018 / Article / Tab 5

Research Article

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

Table 5

Number of ROIs resulted in FP reduction using sparse curvelet coefficient-based LBP & ANN classification at training and validation stage.

ClassDataset usedBenign/malignant massNonmass/benign massTotal (#) imagesTPR (true-positive rate) = TP/#lesionsFPPI (false-positive per image) = FP/# images
Previous stageSelected (TP)Lost (FN)Previous stageSelected (TN)Lost (FP)

Normal vs abnormalMIAS108 ∗ 2 = 216201152732658315(201/216) = 0.93(8/315) = 0.02
DDSM140 ∗ 4 = 560516441203115548240(516/560) = 0.92(48/240) = 0.2

Benign vs malignantMIAS4948159591108(48/49) = 0.98(1/108) = 0.01
DDSM46 ∗ 2 = 9289394895140(89/92) = 0.97(5/140) = 0.03
Normal vs malignantMIAS49 ∗ 4 = 196192427325914256(192/196) = 0.98(14/256) = 0.05
DDSM46 ∗ 4 = 18418221203116736146(182/184) = 0.99(36/146) = 0.25
TMCH: Scanner1107 ∗ 4 = 428424460559312217(424/428) = 0.99(12/217) = 0.05
TMCH: Scanner265 ∗ 4 = 26026002322320135(260/260) = 1.00(0/135) = 0

Augmentation of image.

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