Mammogram Image Classification Using Machine Learning
Keywords:
Genetic Algorithm, feature selection, benign, malignant, Adaboost classifierAbstract
All over the world, breast cancer is the second leading cause of death in women above 40 years of age. To design an efficient classification system for breast cancer diagnosis, one has to use efficient algorithms for feature selection to reduce the feature space of mammogram classification. The current work investigates the use of the hybrid genetic ensemble method for feature selection and classification of masses. A genetic algorithm (GA) is used to select a subset of features and to evaluate the fitness of the selected features, Adaptive boosting (AdaBoost). The selected features are used to classify masses into benign or malignant using AdaBoost classifiers. The results obtained with the proposed method are better when compared with extant research work.
References
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