Convolutional neural network for automated mass segmentation in mammography.

ABSTRACT:

Background

Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC).

Results

We compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolut

SUBMITTER: Abdelhafiz D 

PROVIDER: S-EPMC7724817 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature