Using Machine Learning to Automate Mammogram Images Analysis
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) : 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. How...
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ftdatacite:10.13016/m2elrn-rfks 2023-05-15T17:22:32+02:00 Using Machine Learning to Automate Mammogram Images Analysis Tang, Xuejiao Zhang, Liuhua Zhang, Wenbin Huang, Xin Iosifidis, Vasileios Liu, Zhen Zhang, Mingli Messina, Enza Zhang, Ji 2021 https://dx.doi.org/10.13016/m2elrn-rfks https://mdsoar.org/handle/11603/21574 unknown IEEE This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. computer-aided automatic mammogram analysis system mammogram image classification stages CreativeWork article 2021 ftdatacite https://doi.org/10.13016/m2elrn-rfks 2021-11-05T12:55:41Z 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) : 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. Article in Journal/Newspaper Newfoundland DataCite Metadata Store (German National Library of Science and Technology) Canada Newfoundland |
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topic |
computer-aided automatic mammogram analysis system mammogram image classification stages |
spellingShingle |
computer-aided automatic mammogram analysis system mammogram image classification stages Tang, Xuejiao Zhang, Liuhua Zhang, Wenbin Huang, Xin Iosifidis, Vasileios Liu, Zhen Zhang, Mingli Messina, Enza Zhang, Ji Using Machine Learning to Automate Mammogram Images Analysis |
topic_facet |
computer-aided automatic mammogram analysis system mammogram image classification stages |
description |
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) : 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. |
format |
Article in Journal/Newspaper |
author |
Tang, Xuejiao Zhang, Liuhua Zhang, Wenbin Huang, Xin Iosifidis, Vasileios Liu, Zhen Zhang, Mingli Messina, Enza Zhang, Ji |
author_facet |
Tang, Xuejiao Zhang, Liuhua Zhang, Wenbin Huang, Xin Iosifidis, Vasileios Liu, Zhen Zhang, Mingli Messina, Enza Zhang, Ji |
author_sort |
Tang, Xuejiao |
title |
Using Machine Learning to Automate Mammogram Images Analysis |
title_short |
Using Machine Learning to Automate Mammogram Images Analysis |
title_full |
Using Machine Learning to Automate Mammogram Images Analysis |
title_fullStr |
Using Machine Learning to Automate Mammogram Images Analysis |
title_full_unstemmed |
Using Machine Learning to Automate Mammogram Images Analysis |
title_sort |
using machine learning to automate mammogram images analysis |
publisher |
IEEE |
publishDate |
2021 |
url |
https://dx.doi.org/10.13016/m2elrn-rfks https://mdsoar.org/handle/11603/21574 |
geographic |
Canada Newfoundland |
geographic_facet |
Canada Newfoundland |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_rights |
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. |
op_doi |
https://doi.org/10.13016/m2elrn-rfks |
_version_ |
1766109250997714944 |