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Mass Characterization in Mammograms using an Optimal Ensemble Classifier

Publication Type : Conference Paper

Publisher : 2016 IEEE Region 10 Conference (TENCON)

Source : 2016 IEEE Region 10 Conference (TENCON) (2016)

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Keywords : Artificial neural networks, breast cancer, Computer aided diagnosis, Ensemble, Feature extraction, Genetic algorithm, Genetic algorithms, image classification, Mammograms, mammography, mass characterization, Medical Image Processing, Multicollinearity, optimal ensemble classifier, Support vector machines, Variation Inflation Factor, variation inflation factor analysis, VIF analysis

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2016

Abstract : Mass is the most common indicator in mammograms, especially in the early stages of breast cancer. Due to subtle nature of the masses, there is a considerable overlap between the malignant and benign mass characteristics. In this work, a Computer Aided Diagnosis (CADx) system that employs ensemble classifier has been proposed to improve mass characterization. Genetic algorithm (GA), an optimization technique, was employed to select the optimal ensemble. Multicollinearity among classifiers has to be resolved while forming the ensemble. Combining the classifiers that are highly correlated will not guarantee an improved performance when compared to individual classifiers. Variation Inflation Factor (VIF) analysis is incorporated in this work for detecting multicollinearity among classifiers.

Cite this Research Publication : D. Balachandran and Dr. Lavanya R., “Mass Characterization in Mammograms using an Optimal Ensemble Classifier”, in 2016 IEEE Region 10 Conference (TENCON), 2016.

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