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.