We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests.
- Authors
Singh, Vibhav Prakash; Srivastava, Subodh; Srivastava, Rajeev
- Abstract
Mammogram classification is a crucial and challenging problem, because it helps in early diagnosis of breast cancer and supports radiologists in their decision to analyze similar mammograms out of a database by recognizing the classes of current mammograms. This paper proposes an effective method for classifying mammograms using random forests with wavelet based center-symmetric local binary pattern (WCS-LBP). To classify mammograms, multi-resolution CS-LBP texture characteristics from non-overlapping regions of the mammograms are captured. Further, we examine most relevant features using support vector machine-recursive feature elimination (SVM-RFE). Finally, we feed the selected features to decision trees and construct random forests which are an ensemble of random decision trees. Using wavelet based local CS-LBP features with random forest, we classify the test images into different categories having the maximum posterior probability. The proposed method shows the improved performance as compared with other variant features and state-of-art methods. The obtained performance measures are 97.3% accuracy, 97.3% precision, 97.2% recall, 97.2% F-measure and 94.1% Matthews correlation coefficient (MCC).
- Subjects
BREAST cancer patients; BREAST cancer treatment; MAMMOGRAMS; RANDOM forest algorithms; PROBABILITY theory
- Publication
Technology & Health Care, 2017, Vol 25, Issue 4, p709
- ISSN
0928-7329
- Publication type
journal article
- DOI
10.3233/THC-170851