基于结构自适应模糊神经网络的前列腺癌诊断方法

前列腺癌是近年来严重危害男性健康的疾病.利用模糊神经网络方法可以实现前列腺癌诊断,并将诊断模型表示为模糊规则集合.针对模糊神经网络所提取规则解释性差的问题,提出结构自适应模糊神经网络方法,通过改进损失函数,在训练中控制相似隶属度函数的合并,实现模糊神经网络模型结构自适应调整,减少模糊规则数量,在保证诊断准确性情况下,提取出容易理解的可解释性规则.同时该方法在模型的训练过程中引入粒子群优化(PSO)算法进行结构和参数学习,有效减少计算量,提高训练效率.最后,使用临床医学科学数据中心提供的前列腺疾病检查数据进行数值实验,验证了所提出方法在前列腺癌诊断和可解释性规则提取中的有效性.

Prostate cancer diagnosis method based on structure adaptive fuzzy neural network

Men's health has been seriously damaged due to prostate cancer in recent years. Fuzzy neural network can be used to diagnose prostate cancer, and fuzzy rules can be extracted from the diagnosis model. In order to solve the problem with low interpretable rules extracted by fuzzy neural network, a structure adaptive fuzzy neural network (SAFNN) method is proposed. By modifying the loss function, this method can control the combination of similar membership functions, adjust the structure of fuzzy neural networks adaptively and reduce the number of fuzzy rules in the process of model training. Moreover, this method can extract interpretable rules and guarantee the diagnosis accuracy. To simplify the calculation process and improve training efficiency, particle swarm optimization (PSO) algorithm is adopted to train the structure and parameters of the model. We also conduct experiment studies with the inspection data of prostate diseases provided by National Clinical Medicine Information Center. The experiment result verifies the efficiency of the proposed method in prostate cancer diagnosis and interpretable rules extraction.