Selvakumari Jeya I Jasmine, Deepa S N
Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu 641 032, India.
Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore, Tamil Nadu 641 046, India.
Comput Math Methods Med. 2016;2016:7493535. doi: 10.1155/2016/7493535. Epub 2016 Nov 30.
A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.
一种基于实数编码遗传算法的径向基函数神经网络分类器被用于对健康和患癌肺部图像进行有效分类。提出实数编码遗传算法（RCGA）以克服二进制编码遗传算法（BCGA）遇到的汉明悬崖问题。选择径向基函数神经网络（RBFNN）分类器作为分类模型，是因为其高斯核函数以及有效的学习过程，可避免局部和全局最小值问题并实现更快收敛。本文特别关注使用所提出的RCGA对RBFNN分类器的权重和偏差进行调整。RCGA中使用的算子使算法流程能够计算权重和偏差值，从而获得最小均方误差（MSE）。利用来自肺部图像数据库联盟（LIDC）数据库和实时数据库的健康和癌症肺部图像，注意到所提出的基于RCGA的RBFNN分类器对健康肺组织和患癌肺结节进行了有效分类。与文献中先前提出的分类器相比，使用所提出方法计算的分类准确率更高。
