Optimization for lung cancer prediction using support vector machine with artificial neural networks—A review

Abstract

Lung cancer is not an accidental death nowadays. After undergoing many researches, it is observed that death rate is increased. So, to reduce the death rate, people should go through some quicker diagnosis. Generally, lung cancer detection is done by using many techniques of different domains. Currently, there are many algorithms in use to detect the lung cancer for feature extraction and selection in the domain of machine learning. For segmentation purpose, super-pixel segmentation is highly used. In this paper, we are going to propose the algorithm that is used to show accurate results in the detection of lung cancer. To find a algorithm or optimizing the existing algorithms like genetic optimization algorithms, PSO, SVM and comparing these algorithms with the existing ones then finalizing the standard algorithm that gives accuracy in formation of lung cancer detection system.