Early detection of lung cancer using wavelet feature descriptor and feed forward back propagation neural networks classifier

Abstract

A Computed Tomography (CT) scan is the most used technique for distinguishing harmful lung cancer nodules. A Computer Aided Diagnosis (CAD) framework for the recognition of lung nodules in thoracic CT pictures is implemented. A lung nodule which can be either benign or malignant can be easily classified as the better support for treatment. The proposed work is based on using Wavelet feature descriptor and combined with an Artificial Neural network for classification. The computed statistical attributes such as Autocorrelation, Entropy, Contrast, and Energy are obtained after applying wavelet transform and used as input parameters for neural network Classifier. The NN Classifier is designed by considering training functions (Traingd, Traingda, Traingdm, and Traingdx) using feed forward neural network and feed forward back propagation network. The feed forward back propagation neural network gives better classification results than feed forward. The proposed classifier produced Accuracy of 92.6%, specificity of 100% and sensitivity of 91.2% and a mean square error of 0.978.