Azami Hamed, Escudero Javier
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2836-9. doi: 10.1109/EMBC.2015.7318982.
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.
乳腺癌是全球女性中最常见的癌症类型之一。这种癌症的早期诊断可显著提高长期生存几率。由于乳腺癌的诊断是一个复杂问题，神经网络（NN）方法已被视为一种有前景的解决方案。考虑到反向传播（BP）算法训练前馈神经网络的速度较慢，我们针对威斯康星乳腺癌数据集考虑了多种改进的神经网络训练方法：带动量的BP算法、带自适应学习率的BP算法、带自适应学习率和动量的BP算法、波拉克-里比克共轭梯度算法（CGA）、弗莱彻-里夫斯CGA算法、鲍威尔-比尔CGA算法、缩放CGA算法、弹性BP算法（RBP）、一步割线法和拟牛顿法。神经网络集成是一种将多个神经网络输出进行组合的学习范式，用于提高分类任务的准确性。结果表明，基于神经网络集成的分类方法比基于神经网络的算法具有更好的性能。对于50%-50%训练-测试评估方法，由RBP训练的神经网络集成获得的最高总体平均准确率为97.68%。
