Early Detection and Prediction of Lung Cancer Survival using Neural Network Classifier
Sri Guru, Granth Sahib · 2013
The early detection of lung cancer is a challenging problem, due to the structure of the cancer cells, where most of the cells are overlapped with each other. Classification is very important part of digital image analysis. It is a computational procedure that sort images into groups according to their similarities. In this paper Histogram Equalization is used for preprocessing of the images and feature extraction process and neural network classifier to check the state of a patient in its early stage whether it is normal or abnormal. After that we predict the survival rate of a patient by extracted features. Experimental analysis is made with dataset to evaluate the performance of the different classifiers. The performance is based on the correct and incorrect classification of the classifier. All experiments are conducted in WEKA data mining tool.