A Comparative Study of Breast Cancer Diagnosis based on Neural Network Ensemble via Improved Training Algorithms

Hamed Azami, Javier Escudero

Research output: Contribution to conferencePaperpeer-review

Abstract

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak–Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell–Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.
Original languageEnglish
Pages2836-2839
Number of pages4
Publication statusPublished - 27 Aug 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - MiCo, Milan, Italy
Duration: 26 Aug 201529 Aug 2015

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CountryItaly
CityMilan
Period26/08/1529/08/15

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