摘要
针对深度信念网络(DBN)权值随机初始化易使网络陷入局部最优的问题,在传统DBN模型中引入布谷鸟搜索(CS)算法,提出一种基于CS-DBN的肺部肿瘤图像识别算法。首先,利用CS的全局寻优能力对DBN的初始权值进行优化,并在此基础上进行DBN的逐层预训练;然后,利用反向传播(BP)算法对整个网络进行微调,从而使网络权值达到最优;最后,将CS-DBN应用于肺部肿瘤图像的识别,实验从受限玻尔兹曼机(RBM)训练次数、训练批次大小、DBN隐层层数和隐层节点数四个角度将CS-DBN与传统DBN进行比较,以验证该算法的可行性和有效性。实验结果表明,CS-DBN的识别精度明显高于传统DBN,在不同RBM训练次数、训练批次大小、DBN隐层层数和隐层节点数条件下,CS-DBN较传统DBN识别率提高百分点的范围分别是1.13～4.33、2.00～3.34、1.07～3.34和1.40～3.34。CS-DBN能够在一定程度上提高肺部肿瘤的识别精度,从而提高肺部肿瘤计算机辅助诊断性能。
Due to random initialization of the weights,Deep Belief Network(DBN)easily falls into a local optimum,the Cuckoo Search(CS)algorithm was introduced into the traditional DBN model and a lung cancer image recognition algorithm based on CS-DBN was proposed.Firstly,the global optimization ability of CS was used to optimize initial weights of DBN,and on this basis,the layer-by-layer pre-training of DBN was performed.Secondly,the whole network was fine-tuned by using Back Propagation(BP)algorithm,so that the network weights were optimized.Finally,the CS-DBN was applied to the identification of lung tumor images,and CS-DBN was compared with traditional DBN from the four perspectives of Restricted Boltzmann Machine(RBM)training times,training batch sizes,DBN hidden layers numbers,and hidden layer nodes to verify the feasibility and effectiveness of the algorithm.The experimental results show that the recognition accuracy of CS-DBN is obviously higher than that of traditional DBN.Under the conditions of different RBM training times,training batch sizes,DBN hidden layer numbers,and hidden layer nodes,the increase range of CS-DBN identification accuracy over traditional DBN are 1.13 to 4.33,2 to 3.34,1.07 to 3.34 and 1.4 to 3.34 percentage points respectively.CS-DBN can improve the accuracy of lung tumor recognition to a certain extent,thereby improving the performance of computer-aided diagnosis of lung tumors.
作者
杨健
周涛
郭丽芳
张飞飞
梁蒙蒙
YANG Jian;ZHOU Tao;GUO Lifang;ZHANG Feifei;LIANG Mengmeng(Public Administration Research Center,Ningxia Medical University,Yinchuan Ningxia 750000,China;College of Science,Ningxia Medical University,Yinchuan Ningxia 750000,China)
出处
《计算机应用》
CSCD
北大核心
2018年第11期3225-3230,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61561040)~~
关键词
布谷鸟搜索算法
深度信念网络
受限玻尔兹曼机
肺部肿瘤
图像识别
Cuckoo Search(CS)algorithm
Deep Belief Network(DBN)
Restricted Boltzmann Machine(RBM)
lung tumor
image recognition
