Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 |
| Editors | Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 757-764 |
| Number of pages | 8 |
| ISBN (electronic) | 9781728162157 |
| ISBN (print) | 978-1-7281-6216-4 |
| Publication status | Published - 2020 |
| Event | 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: 16 Dec 2020 → 19 Dec 2020 |
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.
Keywords
- automated diagnostic system, Breast cancer
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Decision Sciences(all)
- Information Systems and Management
- Medicine(all)
- Medicine (miscellaneous)
- Medicine(all)
- Health Informatics
Sustainable Development Goals
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Proceedings: 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 757-764 9313247.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Using Machine Learning to Automate Mammogram Images Analysis
AU - Tang, Xuejiao
AU - Zhang, Liuhua
AU - Zhang, Wenbin
AU - Huang, Xin
AU - Iosifidis, Vasileios
AU - Liu, Zhen
AU - Zhang, Mingli
AU - Messina, Enza
AU - Zhang, Ji
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - automated diagnostic system
KW - Breast cancer
UR - http://www.scopus.com/inward/record.url?scp=85100348811&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2012.03151
DO - 10.48550/arXiv.2012.03151
M3 - Conference contribution
AN - SCOPUS:85100348811
SN - 978-1-7281-6216-4
SP - 757
EP - 764
BT - Proceedings
A2 - Park, Taesung
A2 - Cho, Young-Rae
A2 - Hu, Xiaohua Tony
A2 - Yoo, Illhoi
A2 - Woo, Hyun Goo
A2 - Wang, Jianxin
A2 - Facelli, Julio
A2 - Nam, Seungyoon
A2 - Kang, Mingon
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Y2 - 16 December 2020 through 19 December 2020
ER -