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Table 1 The performance of the proposed Vanilla U-Net model, original U-Net, Faster R-CNN, SegNet, Dilated-Net, FCN, and RG

From: Convolutional neural network for automated mass segmentation in mammography

Model Mean accuracy Mean DI Mean IOU Mean BFscore Mean inference time (second)/image
-Proposed Vanilla U-Net, with Aug. 0.926 0.951 0.909 0.964 0.115
-Proposed Vanilla U-Net, without Aug. 0.910 0.922 0.856 0.940 0.115
-Original U-Net, with Aug. 0.842 0.818 0.693 0.800 0.118
-Original U-Net, without Aug. 0.821 0.801 0.668 0.776 0.118
-Faster R-CNN, with Aug. 0.702 - 0.601 - 0.454
-SegNet, with Aug. 0.853 0.824 0.701 0.822 0.098
-Dilated-Net, with Aug. 0.832 0.799 0.665 0.701 0.094
-FCN, with Aug. 0.843 0.802 0.669 0.752 0.091
-RG 0.801 0.602 0.401 0.603 0.320