Using Machine Learning to Automate Mammogram Images Analysis

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dc.contributor.authorTang, Xuejiao
dc.contributor.authorZhang, Liuhua
dc.contributor.authorZhang, Wenbin
dc.contributor.authorHuang, Xin
dc.contributor.authorIosifidis, Vasileios
dc.contributor.authorLiu, Zhen
dc.contributor.authorZhang, Mingli
dc.contributor.authorMessina, Enza
dc.contributor.authorZhang, Ji
dc.date.accessioned2021-05-20T15:27:50Z
dc.date.available2021-05-20T15:27:50Z
dc.date.issued2021-01-13
dc.description2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)en_US
dc.description.abstractBreast 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.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9313247en_US
dc.description.urihttps://arxiv.org/abs/2012.03151
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2elrn-rfks
dc.identifier.citationTang, 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; 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); https://ieeexplore.ieee.org/document/9313247;en_US
dc.identifier.urihttps://doi.org/10.1109/BIBM49941.2020.9313247
dc.identifier.urihttp://hdl.handle.net/11603/21574
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis 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.
dc.subjectcomputer-aided automatic mammogram analysis system
dc.subjectmammogram image classification stages
dc.titleUsing Machine Learning to Automate Mammogram Images Analysisen_US
dc.typeTexten_US

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