Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.

中文翻译：

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在MLP神经网络中使用多阶段权重调整进行乳腺癌诊断

乳腺癌是最常见的癌症，是全世界女性死亡的原因。有证据表明，早期发现和治疗可以提高患有这种疾病的患者的存活率。因此，本文提出了一种使用遗传算法的自动乳腺癌诊断技术，以同时进行多层感知器（MLP）神经网络的特征选择和参数优化。本文的目的是在两部分中提出一种基于MLP（MWAMLP）神经网络中基于多阶段权重调整的混合分类算法，以提高乳腺癌的诊断率。在第一部分中，在学习数据集上同时训练了三个分类器。第一部分分类器的输出与学习数据集一起放置在新数据集中。该数据集使用混合分类器方法来对第一部分的每个普通分类器的输出与实际输出标签之间的映射进行建模。所提出的算法是通过三种不同的反向传播（BP）技术实现的，即Levenberg-Marquardt，弹性BP和带有动量的梯度下降，用于微调MLP神经网络的权重，并比较了它们的性能。有趣的是，一种名为MWAMLP-RP的拟议算法在威斯康星州乳腺癌数据库数据集上分别产生了最佳和平均正确分类，分别为99.35％和98.74％，这与标题为GP-DLNN的方法所获得的结果相当，GAANN和CAFS等文献中发现的著作。