Rahmani Seryasat O, Haddadnia J. Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.
Clin Breast Cancer 2017;
18:e407-e420. [PMID:
29141776 DOI:
10.1016/j.clbc.2017.05.009]
[Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 04/29/2017] [Accepted: 05/10/2017] [Indexed: 11/17/2022]
Abstract
BACKGROUND
Mammography is the most common screening method for diagnosis of breast cancer.
MATERIALS AND METHODS
In this study, a computer-aided system for diagnosis of benignity and malignity of the masses was implemented in mammogram images. In the computer aided diagnosis system, we first reduce the noise in the mammograms using an effective noise removal technique. After the noise removal, the mass in the region of interest must be segmented and this segmentation is done using a deformable model. After the mass segmentation, a number of features are extracted from it. These features include: features of the mass shape and border, tissue properties, and the fractal dimension. After extracting a large number of features, a proper subset must be chosen from among them. In this study, we make use of a new method on the basis of a genetic algorithm for selection of a proper set of features. After determining the proper features, a classifier is trained.
RESULTS
To classify the samples, a new architecture for combination of the classifiers is proposed. In this architecture, easy and difficult samples are identified and trained using different classifiers. Finally, the proposed mass diagnosis system was also tested on mini-Mammographic Image Analysis Society and digital database for screening mammography databases.
CONCLUSION
The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy.
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