A kernel approach for ensemble decision combinations with two-view mammography applications. (9th December 2010)

• Record Type: Journal Article
• Authors: Land Jr., Walker H.; Margolis, Dan; Kallergi, Maria; Heine, John J.
• Is Part Of: International journal of functional informatics and personalised medicine. Volume 3:Number 2(2010)
• Page Start: 157
• Page End: 182
• Publication Date: 2010-12-09
• Subjects: statistical estimation theory -- statistical learning -- ensemble combinations -- experts mixture -- automated mammographic diagnosis -- classification -- personalised medicine -- differential evolution -- kernel density -- mammography -- mammograms -- benign lesions -- artificial neural networks -- ANNs -- malignant lesions -- breast lesions -- cancer care strategies -- breast cancer; Biomedical engineering -- Periodicals; Bioinformatics -- Periodicals; 610.28
• Languages: English
• ISSNs: 1756-2104

Abstract

An ensemble decision combination method was derived with kernel methods. An ensemble comprised of six Artificial Neural Networks (ANNs) was used to make benign?malignant predictions for breast lesions. The combination processing was evaluated for both single-view and two-view mammograms. The single-view combination showed marked improvements over the performance of each individual ANN, and the two-view combination showed marked improvements over the single-view combination performance. The work provides preliminary validation of the ensemble combination mechanism using an important clinically relevant data set.