This paper proposes a novel ensemble technique for mass classification in digital mammograms by varying the number of hidden units to create diverse candidates. The effects of adding more networks to the ensemble are evaluated on a mammographic database and the results are presented. A classification accuracy of ninety nine percent is achieved. © Springer-Verlag 2013.
CITATION STYLE
Mc Leod, P., & Verma, B. (2013). Effects of large constituent size in variable neural ensemble classifier for breast mass classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8228 LNCS, pp. 525–532). https://doi.org/10.1007/978-3-642-42051-1_65
Mendeley helps you to discover research relevant for your work.