Using Machine Learning to Automate Mammogram Images Analysis

Tang, Xuejiao and Zhang, Liuhua and Zhang, Wenbin and Huang, Xin and Iosifidis, Vasileios and Liu, Zhen and Zhang, Mingli and Messina, Enza and Zhang, Ji (2021) Using Machine Learning to Automate Mammogram Images Analysis. In: 2020 International Conference on Bioinformatics and Biomedicine (BIBM'20), 16-19 Dec 2020, Seoul, South Korea.


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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 18 Feb 2021 06:40
Last Modified: 18 Feb 2021 06:40
Uncontrolled Keywords: Breast cancer, automated diagnostic system, mammography, x-ray imaging
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science
Identification Number or DOI: https://doi.org/10.1109/BIBM49941.2020.9313247
URI: http://eprints.usq.edu.au/id/eprint/41393

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