Using Machine Learning to Automate Mammogram Images Analysis

authored by: Xuejiao Tang, Liuhua Zhang, Wenbin Zhang, Xin Huang, Vasileios Iosifidis, Zhen Liu, Mingli Zhang, Enza Messina, Ji Zhang
Abstract: Breast cancer is the second leading cause of cancer-related death after lung cancer in women. Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate since 1989. However, a relatively high false positive rate and a low specificity in mammography technology still exist. In this work, a computer-aided automatic mammogram analysis system is proposed to process the mammogram images and automatically discriminate them as either normal or cancerous, consisting of three consecutive image processing, feature selection, and image classification stages. In designing the system, the discrete wavelet transforms (Daubechies 2, Daubechies 4, and Biorthogonal 6.8) and the Fourier cosine transform were first used to parse the mammogram images and extract statistical features. Then, an entropy-based feature selection method was implemented to reduce the number of features. Finally, different pattern recognition methods (including the Back-propagation Network, the Linear Discriminant Analysis, and the Naive Bayes Classifier) and a voting classification scheme were employed. The performance of each classification strategy was evaluated for sensitivity, specificity, and accuracy and for general performance using the Receiver Operating Curve. Our method is validated on the dataset from the Eastern Health in Newfoundland and Labrador of Canada. The experimental results demonstrated that the proposed automatic mammogram analysis system could effectively improve the classification performances.
Organisation(s): L3S Research Centre
External Organisation(s): Memorial University of Newfoundland
University of Maryland Baltimore County
Guangdong College of Pharmacy
McGill University
University of Milan - Bicocca
University of Southern Queensland
Type: Conference contribution
Pages: 757-764
No. of pages: 8
Publication date: 2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Computer Science Applications, Information Systems and Management, Medicine (miscellaneous), Health Informatics
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.48550/arXiv.2012.03151 (Access: Open)
https://doi.org/10.1109/BIBM49941.2020.9313247 (Access: Closed)