Open AccessClassification of breast mass in two‐view mammograms via deep learning
Author(s) -
Li Hua,
Niu Jing,
Li Dengao,
Zhang Chen
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12035
Subject(s) - mammography , artificial intelligence , deep learning , breast cancer , convolutional neural network , computer science , pattern recognition (psychology) , digital mammography , artificial neural network , machine learning , radiology , medicine , cancer
Breast cancer is the second deadliest cancer among women. Mammography is an important method for physicians to diagnose breast cancer. The main purpose of this study is to use deep learning to automatically classify breast masses in mammograms into benign and malignant. This study proposes a two‐view mammograms classification model consisting of convolutional neural network (CNN) and recurrent neural network (RNN), which is used to classify benign and malignant breast masses. The model is composed of two branch networks, and two modified ResNet are used to extract breast‐mass features of mammograms from craniocaudal (CC) view and mediolateral oblique (MLO) view, respectively. In order to effectively utilise the spatial relationship of the two‐view mammograms, gate recurrent unit (GRU) structures of RNN is used to fuse the features of the breast mass from the two‐view. The digital database for screening mammography (DDSM) be used for training and testing our model. The experimental results show that the classification accuracy, recall and area under curve (AUC) of our method reach 0.947, 0.941 and 0.968, respectively. Compared with previous studies, our method has significantly improved the performance of benign and malignant classification.