Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data

Abstract

Lung cancer was the most commonly diagnosed cancer as well as the leading cause of cancer death in males in 2008 globally. The way used to detect lung cancer are through examination chest X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging results. The accurate and efisien analysis of the imaging results are important to ensure the minimal time processing. A computed assisted diagnosis system is the crusial research which can conduct the analysis efficiently and efectively. This paper aimed to compare the classification performances of Multi Layered Perceptron (MLP) and Radial Basis Function (RBF) techniques. The public lung cancer datasets was used as training and testing data in the classfication techniques. Ten fold cross validation was used for dividing data before classifying techniques. The accuracy performances are compared to check a better technique for classification step.