Detecting and treating breast cancer at earlier stages is highly proved to improve the survival rate of breast cancer patients as breast cancer is considered a major cause of death worldwide. Classical methods for diagnosing breast cancer depend on human expertise and they incur huge amounts of labor, time and are subject to human error. An Integrated Artificial Immune system and Artificial Bee Colony based breast cancer diagnosis (IAIS-ABC-CDS) is proposed for parallel processing of effective feature selection and parameter optimization in an Artificial Neural Network (ANN). The IAIS-ABC-CDS with Momentum-based Gradient Descent Backpropagation (MBGD) that uses the advantages of Simulated Annealing (SA) for enhancing local search process is compared to the benchmark diagnosis schemes of IAIS-ABC-CDS with Resilient Back-Propagation Techniques (RBPT) and Genetic Algorithm based ANN with Multilayer Perceptron (GA-ANN-MLP) schemes. The proposed IAIS-ABC-CDS is confirmed to produce a mean classification of 99.34% and 99.11% in ANN under the Wisconsin dataset.

事实证明，在早期发现和治疗乳腺癌可以提高乳腺癌患者的生存率，因为乳腺癌被认为是世界范围内的主要死亡原因。诊断乳腺癌的经典方法取决于人类的专业知识，它们招致大量的劳动，时间并且容易出错。提出了一种集成的人工免疫系统和基于人工蜂群的乳腺癌诊断（IAIS-ABC-CDS），用于在人工神经网络（ANN）中对有效特征选择和参数优化进行并行处理。将具有基于动量的梯度下降反向传播（MBGD）的IAIS-ABC-CDS和利用弹性反向传播技术的IAIS-ABC-CDS的基准诊断方案进行了比较，该方法利用了模拟退火（SA）的优势来增强本地搜索过程。 （RBPT）和基于遗传算法的具有多层感知器的ANN（GA-ANN-MLP）方案。在威斯康星州数据集下，所提出的IAIS-ABC-CDS在ANN中得到了平均分类，确认为99.34％和99.11％。