A Wrapper Based Binary Shuffled Frog Algorithm for Efficient Classification of Mammograms

Background: Breast cancers are conventionally the leading cause of cancer-related deaths amongst women. For the reduction of death rates by earlier identification of carcinogenic areas, mammogram images are utilized. Computer aided diagnosis plays an important role in screening of the mammograms. Mammography is an efficient as well as feasible method for the detection of breast cancers, especially minute tumours. Efficient performance of these tools is dependent on the efficacy of the classifier algorithms. In this work, feature selection techniques are proposed to improve the efficacy of the classifiers. Method: The major phases in diagnosing breast cancers are features extraction and selection. Detecting tumours naturally requires extraction of features as well as their classification. This work uses Pseudo Zernike Moments and Gaussian Markov Random Field (GMRF) for feature extraction, Binary Shuffled frog algorithm, Information Gain (IG) and Binary Particle Swarm Optimization (PSO) for feature selection and C4.5, Random tree, Adaboost as classifiers. Results: The use of feature selection techniques for successfully selecting relevant feature subset improves the classification performance. The proposed wrapper based Binary Shuffled Frog Algorithm improves the detection of breast cancer mass in mammograms. Conclusion: The work focuses on improving classification performance through feature selection. The experimental results demonstrate the efficacy of the proposed feature selection method. It is observed that the feature selectors are necessary to improve the efficiency of the classifiers. It is observed that among the various classification techniques, C4.5 outperforms other algorithms achieving the highest accuracy.