Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning

摘要

The proposed diagram for the CAD system methodology, based on a U-Net model for mass detection and segmentation. From left to right: original mammogram, pre-processing phase (noise removal, application of CLAHE, and resizing), data augmentation (horizontal inversion with zoom 0.2/0.15 and generated ROI), training (80% of cases), and test (20% of cases)