Using Machine Learning to Automate Mammogram Images Analysis

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 mammograp...

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Bibliographic Details
Published in:2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Main Authors: Tang, Xuejiao, Zhang, Liuhua, Zhang, Wenbin, Huang, Xin, Iosifidis, Vasileios, Liu, Zhen, Zhang, Mingli, Messina, Enza, Zhang, Ji
Format: Text
Language:unknown
Published: 2021
Subjects:
Breast cancer
automated diagnostic system
mammography
x-ray imaging
Canada
Newfoundland
Online Access:https://research.usq.edu.au/item/q63wx/using-machine-learning-to-automate-mammogram-images-analysis
https://doi.org/10.1109/BIBM49941.2020.9313247
Description
Summary: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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