Lung Cancer Classification Using Radial Basis Function Based probabilistic Neural Networks

Abstract

The Automatic Support Intelligent System is used to detect Lung Tumor through the combination of bilateral filteringandneural network system.  It helps in the diagnostic and aid in the treatment of the lung tumor. The detection of the lung Tumor is a challenging problem, due to the structure of the Tumor cells in the lung. This project presents an analytical method that enhances the detection of lung tumor cells in its early stages and to analyze anatomical structures by training and classification of the samples in neural network system and tumor cell segmentation of the sample using clustering algorithm. The artificial neural network will be used to train and classify the stage of Lung Tumor that would be benign, malignant or normal. In lung structure analysis, the lesions which areSolid Nodules and GGO are extracted. Probabilistic Neural Network with radial basis function is employed to implement an automated Lung Tumor classification. Decision making is performed in two stages: feature extraction using GLCM and the classification using PNN-RBF network. The performance of this automated intelligent system evaluates in terms of training performance and classification accuracies to provide the precise and accurate results.